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Abstract

Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.

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... Heart rate variability (HRV) measures the variation between consecutive heartbeats that can be analyzed in both the frequency and time domains [9]. It serves as a non-invasive tool to assess the ANS and is a useful stress indicator [3]. ...
... It serves as a non-invasive tool to assess the ANS and is a useful stress indicator [3]. Factors like anxiety, stress, physical exercise, and heart disease activate the SNS, leading to increased HR [9]. HRV is derived from ECG and BVP signals. ...
... • pNN50%: Number of successive differences of normal RR intervals (∆RR) which differ by more than 50 ms. It is expressed as a percentage of the total number of normal RR intervals [9], where ∆RR j is the difference j-th between two RR intervals. ...
... Previous studies found changes in the autonomic nervous system (ANS) linked with ELA 2 , for example, a decrease in resting state heart rate variability (HRV) 2,5,6 . This implies a distortion of the parasympathetic nervous system (PNS) and, as HRV generally serves as a marker of ANS adaptability 7,8 , could also imply a reduced adaptability to changing environmental demands. Adaptability is an integral part of physical and mental health [7][8][9] , with decreased resting-state HRV linked to ELA and various mental and physiological diseases 7,10,11 . ...
... This implies a distortion of the parasympathetic nervous system (PNS) and, as HRV generally serves as a marker of ANS adaptability 7,8 , could also imply a reduced adaptability to changing environmental demands. Adaptability is an integral part of physical and mental health [7][8][9] , with decreased resting-state HRV linked to ELA and various mental and physiological diseases 7,10,11 . While numerous studies have found a distortion of the affective and physiological reaction to stress related to ELA [7][8][9][10][11][12][13] , there is a considerable lack of studies focusing on the effect on relaxation. ...
... This implies a distortion of the parasympathetic nervous system (PNS) and, as HRV generally serves as a marker of ANS adaptability 7,8 , could also imply a reduced adaptability to changing environmental demands. Adaptability is an integral part of physical and mental health [7][8][9] , with decreased resting-state HRV linked to ELA and various mental and physiological diseases 7,10,11 . While numerous studies have found a distortion of the affective and physiological reaction to stress related to ELA [7][8][9][10][11][12][13] , there is a considerable lack of studies focusing on the effect on relaxation. ...
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While Early Live Adversity (ELA) is a known risk factor for mental and physical diseases, the investigation into the mechanisms behind this connection is ongoing. In the present study, we investigated whether ELA blunts the relaxation response in healthy adults. Using a within-subjects design, we employed a paced breathing exercise (four seconds inhale, six seconds exhale) and a 360° nature video as relaxation interventions while measuring physiological relaxation using heart rate variability and subjective relaxation using the Relaxation State Questionnaire. A total of 103 participants (63.11% female; agemean = 22.73 ± 3.43 years) completed the Parental Bonding Instrument and the Childhood Trauma Questionnaire to assess ELA retrospectively. For subjective relaxation, a blunted relaxation reaction was associated with lower scores of paternal care and higher scores of paternal overprotection, physical abuse, physical neglect, and emotional abuse. For heart rate variability emotional abuse in interaction with nicotine consumption was related to a blunted relaxation response. This indicates that experiencing ELA negatively affects the relaxation capability in a healthy sample and emphasizes the importance of assessing relaxation at a physiological and subjective level.
... Heart rate variability (HRV) is one of the most widely used biometrics for autonomic tone evaluation [1]. It can be quantified in the time, frequency, and nonlinear domain. ...
... It can be quantified in the time, frequency, and nonlinear domain. HRV has been linked to diverse types of physiological and pathological factors, including physical and mental stress, respiratory dynamics, and cardiovascular disorders [1]- [3]. Accordingly, the ambulatory HRV monitoring holds substantial promise as a non-invasive tool for cardiovascular health monitoring. ...
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Heart rate variability (HRV) is widely recognized as a valuable biomarker for assessing autonomic cardiac regulation. Pulse rate variability (PRV) is a common surrogate of HRV given the wide usability of PPG in commercially available devices. However, there is no clear conclusion on whether PRV can replace HRV given their different physiological mechanisms. This study evaluates the interchangeability of young adults HRV and PRV during supine-to-stand (STS) tests which are known as common posture transitions in daily life monitoring. Fifteen features from time, frequency and nonlinear domains were extracted from both electrocardiography and PPG signals. Paired t-tests and Wilcoxon signed-rank tests examined the difference between the extracted HRV and PRV features during supine, transition and standing phases separately. One feature showed significant difference in the supine phase, and this discrepancy increased to four in the transition and standing phases. These findings suggested that PRV is different from HRV in the STS tests, despite the fact that both metrics can reflect the sympathetic activation triggered by the posture changes.
... HRV exhibits abrupt changes in frequency throughout the ECG signal. According to the previous studies, HRV analysis using FT (Fourier transform) and STFT (short-term Fourier transform) has not resulted in positive outcomes due to their limitations such as not being localized in the time domain and having a fixed window size [18,19]. Wavelet transform analysis has emerged as a powerful tool for analyzing HRV and assessing cardiac sympatho-vagal balance with both time-and frequency-localized multi-resolution analysis [20]. ...
... HRV exhibits abrupt changes in frequency throughout the ECG signal. According to the previous studies, HRV analysis using FT (Fourier transform) and STFT (short-term Fourier transform) has not resulted in positive outcomes due to their limitations such as not being localized in the time domain and having a fixed window size [18,19]. Wavelet transform analysis has emerged as a powerful tool for analyzing HRV and assessing cardiac sympatho-vagal balance with both timeand frequency-localized multi-resolution analysis [20]. ...
Article
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Heart rate variability (HRV), which is the variation between consecutive heartbeats, reflects the electrical activity of the heart and provides insight into the autonomic nervous system (ANS) function. This study uses wavelet transform-based HRV feature extraction to investigate cardiac sympatho-vagal balance. Both the continuous wavelet transform (CWT) and discrete wavelet transform (DWT) methods were applied in different steps. DWT was used for R-peak detection and CWT and MODWT were used to generate spectrograms from RR intervals. Frequency components (HF, LF, and VLF) within 0.003–0.4 Hz were extracted, and power estimation was performed. The LF/HF ratio, indicating sympatho-vagal balance, was calculated. ECG data from 42 arrhythmia patients and 18 normal sinus rhythm subjects were analyzed. The results showed a lower LF/HF ratio in arrhythmia patients, with higher HF power in arrhythmia subjects and higher LF power in normal sinus rhythm subjects. The study suggests that the parasympathetic nervous system dominates the ANS in arrhythmia patients, while the sympathetic nervous system dominates in normal sinus rhythm patients.
... Heart rate variability (HRV ) is an established non-invasive method for the assessment of ANS activity. 7 HRV is the change in the R-R beat-to-beat interval, which is related to the interaction between the sympathetic nervous system and the parasympathetic nervous system. 7,8 Several studies have shown that lower HRV was associated with higher cardiovascular events and mortality whereas high HRV showed higher cardiac fitness. ...
... 7 HRV is the change in the R-R beat-to-beat interval, which is related to the interaction between the sympathetic nervous system and the parasympathetic nervous system. 7,8 Several studies have shown that lower HRV was associated with higher cardiovascular events and mortality whereas high HRV showed higher cardiac fitness. 9,10 Previous studies have found that ANS activity fluctuates during the 24-h period like a biological clock, or exhibits a circadian pattern. ...
Article
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Background and Objective Premature ventricular complex (PVC) burden exhibits one of three circadian types, classified as fast-type, slow-type, and independent-type PVC. It is unknown whether PVC circadian types have different heart rate variability (HRV) parameter values. Therefore, this study aimed to evaluate differences in HRV circadian rhythm among fast-, slow-, and independent-type PVC. Methods This cross-sectional observational study consecutively recruited 65 idiopathic PVC subjects (23 fast-, 20 slow-, and 22 independent-type) as well as five control subjects. Each subject underwent a 24-hour Holter to examine PVC burden and HRV. HRV analysis included components that primarily reflect global, parasympathetic, and sympathetic activities. Repeated measures analysis of variance was used to compare differences in HRV circadian rhythm by PVC type. Results The average PVC burden was 15.7%, 8.4%, and 13.6% in fast-, slow-, and independent-type idiopathic PVC subjects, respectively. Global, parasympathetic nervous system, and sympathetic nervous system HRV parameters were significantly lower in independent-type PVC versus fast- and slow-type PVC throughout the day and night. Furthermore, we unexpectedly found that tendency towards sympathetic activity dominance during nighttime was only in independent-type PVC. Conclusion The HRV parameters are reduced in patients with independent-type PVC compared to fast- and slow-type PVC. Future research is warranted to determine possible differences in the prognosis between the three PVC types.
... High frequency is used to reflect respiratory sinus arrhythmia and efference and constitutes an important component of vagal activity [8]. Changes in heart rate variability between high frequencies are affected by parasympathetic activity, while changes between low frequencies are affected by both sympathetic and parasympathetic activity [10]. ...
... Patients were placed in a supine position, the thigh was slightly abducted, and the skin was cleaned using an antiseptic solution. The femoral artery was palpated at 1-2 cm distal to the inguinal ligament, a linear ultrasound probe (8)(9)(10)(11)(12) MHz; Esaote Mylab 30 Ultrasound, Esaote, Fishers, IN, USA) was placed, and the femoral nerve under the fascia iliaca was visualized. A 22-gauge 80 mm block needle (BRAUN Stimuplex ® Ultra 360 ® , Melsungen, Germany) was connected to a nerve stimulator (Stimuplex HNS 11™, B. Braun Medical Inc., Melsungen, Germany) and advanced from lateral to medial with an in-plane technique. ...
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Background and Objectives: The aim of our study is to determine the effects of analgesia nociception index (ANI) monitoring on intraoperative opioid consumption, postoperative analgesia, and the recovery unit length of stay in patients with a preoperative femoral nerve block (FNB) undergoing total knee arthroplasty (TKA) surgery under general anesthesia. Materials and Methods: Seventy-four patients in the American Society of Anesthesiologists Physical Status (ASA-PS) I-III scheduled for TKA under general anesthesia were included in this study. After FNB, the patients were divided into two groups (control group (n = 35)–ANI group (n = 35)). After standard anesthesia induction in both groups, maintenance was conducted using sevoflurane and remifentanil infusion with a bispectral index (BIS) between 40 and 60. In the control group, the intraoperative remifentanil infusion dose was adjusted using conventional methods, and in the ANI group, the dose was adjusted using ANI values of 50–70. The duration of operation, duration of surgery, extubation time, tourniquet duration and pressure, and the amount of remifentanil consumed intraoperatively were recorded. Results: Intraoperative remifentanil consumption was lower in the ANI group compared to the control group (p = 0.001). The time to reach a Modified Aldrete Scale score (MAS) ≥ 9 was shorter in the ANI group (p < 0.001). NRS scores in the recovery unit and 4, 8, 12, and 24 h postoperatively were lower in the ANI group compared to the control group (p = 0.006, p < 0.05). There was a weak significant inverse relationship between the last ANI values measured before extubation and NRS scores in the postoperative recovery unit (r: −0.070–0.079, p: 0.698–0.661). No difference was observed between the groups in other data. Conclusions: In patients undergoing TKA with FNB under general anesthesia, ANI monitoring decreased the amount of opioids consumed intraoperatively and postoperative pain scores and shortened the length of stay in the recovery unit. We suggest that ANI monitoring in intraoperative analgesia management may be helpful in determining the dose of opioid needed by the patient and individualized analgesia management.
... Heart rate variability (HRV) describes the temporal changes between successive R peaks in the QRS complexes of the electrocardiogram (ECG). HRV provides insight into the function of both the cardiovascular system and autonomic nervous system (ANS) through the activity of the sympathetic (SNS) and parasympathetic (PNS) nervous subsystems [1][2][3][4][5][6][7][8][9][10]. The influence of neural drive on the recorded HRV is described In summary, the existing current algorithms operate offline for the vast majority of time, which does not allow for monitoring changes in the frequency components continuously. ...
Article
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Heart rate variability (HRV) containing four components of high (HF), low (LF), very low (VLF), and ultra-low (ULF) frequencies provides insight into the cardiovascular and autonomic nervous system functions. Classical spectral analysis is most often used in research on HRV and its components. The aim of this work was to develop and validate an online HRV decomposition algorithm for monitoring the associated physiological processes. The online algorithm was developed based on variational mode decomposition (VMD), validated on synthetic HRV with known properties and compared with its offline adaptive version AVMD, standard VMD, continuous wavelet transform (CWT), and wavelet package decomposition (WPD). Finally, it was used to decompose 36 real all-night HRVs from two datasets to analyze the properties of the four extracted components using the Hilbert transform. The statistical tests confirmed that the online VMD (VMDon) algorithm returned results of comparable quality to AVMD and CWT, and outperformed standard VMD and WPD. VMDon, AVMD, and CWT extracted four components from the real HRV with frequency content slightly exceeding the previously recognized ranges, suggesting the possibility of their modes mixing. Their ranges of variability were assessed as follows: HF: 0.11–0.40 Hz; LF: 0.029–0.14 Hz; VLF: 4.7–31 mHz; and ULF: 0.002–3.0 mHz.
... Hz) and high frequencies (HF: 0.15-0.4 Hz), respectively [74], although with some variability in the bands' definitions. Because some studies have shown that the estimation of sympathetic activity from HRV can be biased [75], sympathetic markers are also gathered from other physiological activity, such as sympathetic nerve neurogram or electrodermal activity [24]. ...
Article
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Exploring brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, has demonstrated enormous potential in biomarker development and neuroscientific research. A range of techniques, from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from estimating brain responses to individual heartbeats to interactions linking the heart to changes in brain organization. This review provides an overview of the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of physiological disruptions on brain-heart interactions, solidifying it as a biomarker. The vast outlook of these methods could provide tools for disease stratification in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.
... Hz y muy baja frecuencia (VLF), en el rango de 0.0033-0.04 Hz (Rajendra Acharya et al., 2006). ...
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Esperamos que este libro despierte el interés de los lectores y se convierta en un valioso recurso para estudiantes, investigadores y profesionales interesados en ampliar su comprensión de este apasionante campo del conocimiento. También, resaltamos y agradecemos, el gran trabajo que realizaron todos los que contribuyeron a la obra y que hicieron posible ofrecer este producto final. Rosa María Hidalgo y Maryed Rojas
... The analysis of autonomic dynamics through heart rate variability (HRV) is a standard approach for clinical and fundamental research [1][2][3]. Biomarkers based on HRV serve for the non-invasive analysis of physiological responses to different stimuli, which allows the assessment of several pathological conditions [4][5][6]. Additionally, HRV analysis can enable the characterization of neural processes, which can help to enlighten the physiological underpinnings behind homeostatic regulations, sensorimotor function and cognition [7]. ...
Article
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The time-resolved analysis of heart rate (HR) and heart rate variability (HRV) is crucial for the evaluation of the dynamic changes of autonomic activity under different clinical and behavioural conditions. Standard HRV analysis is performed in the frequency domain because the sympathetic activations tend to increase low-frequency HRV oscillations, while the parasympathetic ones increase high-frequency HRV oscillations. However, a strict separation of HRV into frequency bands may cause biased estimations, especially in the low-frequency range. To overcome this limitation, we propose a robust estimator that combines HR and HRV dynamics, based on the correlation of the Poincaré plot descriptors of interbeat intervals from the electrocardiogram. To validate our method, we used electrocardiograms gathered from open databases where standardized paradigms were applied to elicit changes in autonomic activity. Our proposal outperforms the standard spectral approach for the estimation of low- and high-frequency fluctuations in HRV, and its performance is comparable with newer methods. Our method constitutes a valuable, robust, time-resolved and cost-effective tool for a better understanding of autonomic activity through HR and HRV in a healthy state and potentially for pathological conditions.
... P HYSIOLOGICAL signals measurement has long been a significant research domain that can provide insights into the health condition of the human body [1]. Physiological signals such as heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) are particularly crucial for human body health accurate assessment [2], [3]. In recent years, the Remote Photoplethysmography (rPPG) has gained considerable attention as a research focal point [4]- [10]. ...
Preprint
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Remote physiological signal measurement based on facial videos, also known as remote photoplethysmography (rPPG), involves predicting changes in facial vascular blood flow from facial videos. While most deep learning-based methods have achieved good results, they often struggle to balance performance across small and large-scale datasets due to the inherent limitations of convolutional neural networks (CNNs) and Transformer. In this paper, we introduce VidFormer, a novel end-to-end framework that integrates 3-Dimension Convolutional Neural Network (3DCNN) and Transformer models for rPPG tasks. Initially, we conduct an analysis of the traditional skin reflection model and subsequently introduce an enhanced model for the reconstruction of rPPG signals. Based on this improved model, VidFormer utilizes 3DCNN and Transformer to extract local and global features from input data, respectively. To enhance the spatiotemporal feature extraction capabilities of VidFormer, we incorporate temporal-spatial attention mechanisms tailored for both 3DCNN and Transformer. Additionally, we design a module to facilitate information exchange and fusion between the 3DCNN and Transformer. Our evaluation on five publicly available datasets demonstrates that VidFormer outperforms current state-of-the-art (SOTA) methods. Finally, we discuss the essential roles of each VidFormer module and examine the effects of ethnicity, makeup, and exercise on its performance.
... HRV is the variation of time or frequency between each heartbeat (Malik, 1995). HRV provides a reliable reflection of the heart's response to physiological influences, including functions of the ANS (Rajendra, et al., 2006). The ANS regulates heart rate, blood pressure, and respiration and is activated by stress (Waxenbaum, et al., 2020). ...
Article
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Job stress among child welfare professionals affects their mental and physical health and is associated with negative outcomes. Researchers have used self-report measures to document the stress experienced by those in the child welfare field, and this is the first study to use biometric technology across 72 h to identify physiological indicators of stress, recovery, and sleep in frontline child welfare workers in mostly rural areas (n = 32). A stress profile of the participants is presented on their heart rates, mean time-stressed, percent of time stressed, mean time relaxed, percent of time relaxed, mean sleep time, mean RMSSD in sleep, and body mass index (BMI). Variables were also examined by length of employment with the agency. Results indicate participants averaged nearly 16 h of physiological stress per day and were unable to spend much time in relaxation or recovery from stress. Stress appeared ubiquitous and possibly difficult to manage.
... The PNS index in the Kubios HRV software is related to cardiac vagal activity, increasing the average RR interval (i.e., decreasing HR); thus, the average RR interval is a natural choice for calculating the PNS index. Besides the average heart rate, cardiac vagal activity affects heart rate variability by regulating the magnitude of the respiratory sinus arrhythmia (RSA) component [11]. The SNS index in the Kubios HRV software is calculated based on the average HR (bpm) and Baevsky's stress index. ...
Article
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The academic routine, with its responsibilities and commitments, is crucial for professional life but can cause significant stress for students. This study analyzed the stress levels in physiotherapy students at a private college before and after exams. It is an analytical, observational, quantitative, and cross-sectional study conducted with 16 students (10 in the morning shift and 6 in the evening shift) during October 2019. Heart rate variability was measured with a sensor placed on the ear, using the Inner Balance app, before and after the exams. The data were analyzed by Kubius HRV. Additionally, the Lipp's Adult Stress Symptoms Inventory (ISSL) and Whoqol-bref questionnaires were used. The results showed a significant increase in sympathetic nervous system activity after the exams, indicating stress related to the evaluations. 45% of the students exhibited typical symptoms of increased sympathetic activity, corroborating the heart rate variability data. These findings suggest the need for longitudinal studies to understand the duration and effects of academic stress on students' bodies.
... HRV represents the balance between the parasympathetic and sympathetic nervous systems. A nocturnal HRV analysis is widely used as a noninvasive assessment of autonomic function across various fields [17]. Evidence also shows that HRV can evaluate changes in ANS activity and may be a valuable indicator of OSA severity [18][19][20]. ...
Article
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Background/Objectives: One prior study revealed that a newly developed auto-titrating mandibular advancement device (AMAD) could potentially enhance polysomnographic outcomes in individuals with obstructive sleep apnea (OSA). However, evidence regarding its impact on autonomic nervous system dysregulation in OSA remains limited. In this study, we aimed to compare the effects of conventional mandibular advancement devices (MADs) and AMDA on autonomic function. Methods: We retrospectively reviewed data from patients who visited a sleep center with complaints of snoring and sleep apnea (30 and 15 patients in the conventional MAD and AMAD groups, respectively). We assessed heart rate variability (HRV) frequency-domain metrics such as total power (TP), very low frequency (VLF), low frequency (LF), and high frequency (HF) using ultra-short-term and short-term modalities, assessing sympathetic and parasympathetic activity changes across treatment groups. Results: Conventional MAD treatment was associated with reductions in LF and LF/HF ratios, whereas AMAD treatment was linked to decreases in TP, VLF, LF, and LF/HF ratios. Notably, in patients with moderate OSA, LF values were significantly lower in the AMAD group than in the conventional MAD group. Conclusions: These findings suggest that both devices could reduce sympathetic over-activity in patients with OSA, with AMAD demonstrating greater efficacy, particularly in those with moderate OSA.
... Heart rate variability (HRV), the variation in beat-tobeat intervals represented by RR or NN interval [1], displays irregular and non-stationary behaviors whose nonlinear dynamics provide valuable information for cardiac scientific and clinical researches [2,3]. To measure its nonlinear dynamical features, some complexity parameters, such as fractal dimensions, Lyapunov exponents, geometric and entropy methods et al., are proposed [4][5][6]. ...
Preprint
Symbolizations, the base of symbolic dynamic analysis, are classified as global static and local dynamic approaches which are combined by joint entropy in our works for nonlinear dynamic complexity analysis. Two global static methods, symbolic transformations of Wessel N. symbolic entropy and base-scale entropy, and two local ones, namely symbolizations of permutation and differential entropy, constitute four double symbolic joint entropies that have accurate complexity detections in chaotic models, logistic and Henon map series. In nonlinear dynamical analysis of different kinds of heart rate variability, heartbeats of healthy young have higher complexity than those of the healthy elderly, and congestive heart failure (CHF) patients are lowest in heartbeats' joint entropy values. Each individual symbolic entropy is improved by double symbolic joint entropy among which the combination of base-scale and differential symbolizations have best complexity analysis. Test results prove that double symbolic joint entropy is feasible in nonlinear dynamic complexity analysis.
... Cardiologists utilise AR model when they are interested in fit tachograms through mathematical regressions softwares. The tachogram is a signal that allows the study of heart rate variability (HRV) [14,15]. Boardman and collaborators [16] study autoregressive model for the HRV. ...
Preprint
The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to model electrocardiogram signals. Our results are compared with experimental tachogram by means of Poincar\'e plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.
... The influence of the autonomic nervous system on the sinoatrial node varies according to changes in the internal/external environment. This periodic change in heart rate over time is called heart rate variability (HRV), which refers to the small changes between one cardiac cycle and the next 10,11) . A decrease in HRV indicates reduction in the complexity of dynamic changes of the heart rate. ...
Article
Objectives: People generally seek out spicy taste when stressed. Eating spicy food activates the autonomic nervous system, causing the body to sweat and the heart to beat faster. The objective of the study is to investigate the correlation with the autonomic nervous system balance according to the preference for spicy taste using HRV.Method: This study measured the changes in heart rate using SA-3000P in patients who visited the local clinic between January and May 2023. To minimize any fluctuations in the autonomic nervous system before measurement due to external factors, subjects were asked to sit in a chair or lie down to rest for approximately 10 minutes before the test and any jewelry were asked to be removed. The test involved attaching ECG electrodes on both wrists and left ankle to measure the autonomic nervous system indicators by measuring, detecting and recording data for 5 minutes. For HRV measurements, SDNN (standard deviation of all normal R-R intervals) was obtained using time domain analysis, and TP (total power), VLF (very low frequency, 0.003∼0.04 Hz), LF (low frequency, 0.04∼0.15 Hz), HF (high frequency, 0.15∼0.4 Hz), and LF/HF ratio were calculated using frequency domain analysis. Additionally, using the measurements, indicators such as RMSSD (root mean square of the successive difference), autonomic nerve activation, and fatigue were produced.Results: The group that preferred spicy taste showed significantly lower TP, LF, HF, RMSSD, and autonomic nerve activation compared to the control group; VLF was lower but there was no statistically significant difference. Fatigue was higher in the group that preferred spicy taste compared to the control group with statistical significance; LF/HF Ratio was higher but with no statistically significant difference.Conclusion: The preference for spicy taste may cause autonomic nervous system abnormality; thus, it may be advisable to avoid the intake of spicy foods.
... The study of beat-tobeat variations in heart rate is typically referred to under the umbrella term of heart rate variability. See [52,4,5] for a historical perspective on heart rate variability. The nonlinear dynamics community has contributed a large number of methods for the analysis of interbeat intervals. ...
Preprint
We introduce a method for quantifying the inherent unpredictability of a continuous-valued time series via an extension of the differential Shannon entropy rate. Our extension, the specific entropy rate, quantifies the amount of predictive uncertainty associated with a specific state, rather than averaged over all states. We relate the specific entropy rate to popular `complexity' measures such as Approximate and Sample Entropies. We provide a data-driven approach for estimating the specific entropy rate of an observed time series. Finally, we consider three case studies of estimating specific entropy rate from synthetic and physiological data relevant to the analysis of heart rate variability.
... It is well established that HRV increases in response to PNS activity (vagal stimulation) and decreases in response to SNS activity [51][52][53]. Stress also affects HRV, and current neurobiological research supports HRV as an objective indicator of psychological well-being and stress. When an individual is under stress or pressure, the high-frequency component of HRV is decreased [54]. ...
Article
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Tactile interaction plays an essential role in human-to-human interaction. People gain comfort and support from tactile interactions with others and touch is an important predictor for trust. While touch has been explored as a communicative modality in HCI and HRI, we here report on two studies in which touching a social robot is used to regulate people’s stress levels and consequently their actions. In the first study, we look at whether different intensities of tactile interaction result in a physiological response related to stress, and whether the interaction impacts risk-taking behaviour and trust. We let 38 participants complete a balloon analogue risk task (BART), a computer-based game that serves as a proxy for risk-taking behaviour. In our study, participants are supported by a robot during the BART task. The robot builds trust and encourages participants to take more risk. The results show that affective tactile interaction with the robot increases participants’ risk-taking behaviour, but gentle affective tactile interaction increases comfort and lowers stress whereas high-intensity touch does not. We also find that male participants exhibit more risk-taking behaviour than females while being less stressed. Based on this experiment, a second study is used to ascertain whether these effects are caused by the social nature of tactile interaction or by the physical interaction alone. For this, instead of a social robot, participants now have a tactile interaction with a non-social device. The non-social interaction does not result in any effect, leading us to conclude that tactile interaction with humanoid robots is a social phenomenon rather than a mere physical phenomenon.
... It is well established that HRV increases in response to PNS activity (vagal stimulation) and decreases in response to SNS activity [51][52][53]. Stress also affects HRV, and current neurobiological research supports HRV as an objective indicator of psychological well-being and stress. When an individual is under stress or pressure, the highfrequency component of HRV is decreased [54]. ...
Preprint
Full-text available
Tactile interaction plays an essential role in human-to-human interaction. People gain comfort and support from tactile interactions with others and touch is an important predictor for trust. While touch has been explored as a communicative modality in HCI and HRI, we here report on two studies in which touching a social robot is used to regulate people's stress levels and consequently their actions. In the first study, we look at whether different intensities of tactile interaction result in a physiological response related to stress, and whether the interaction impacts risk-taking behaviour and trust. We let 38 participants complete a Balloon Analogue Risk Task (BART), a computer-based game that serves as a proxy for risk-taking behaviour. In our study, participants are supported by a robot during the BART task. The robot builds trust and encourages participants to take more risk. The results show that affective tactile interaction with the robot increases participants' risk-taking behaviour, but gentle affective tactile interaction increases comfort and lowers stress whereas high-intensity touch does not. We also find that male participants exhibit more risk-taking behaviour than females while being less stressed. Based on this experiment, a second study is used to ascertain whether these effects are caused by the social nature of tactile interaction or by the physical interaction alone. For this, instead of a social robot, participants now have a tactile interaction with a non-social device. The non-social interaction does not result in any effect, leading us to conclude that tactile interaction with humanoid robots is a social phenomenon rather than a mere physical phenomenon.
... Influenced by several factors, HRV measures fluctuations in the beating of the heart as a compensatory mechanism to cardiovascular demands and in making proper decisions (Achten & Jeukendrup, 2003;Forte et al., 2021;Fournie et al., 2021;Hisatsune et al., 2022;Malik, 1996;Pham et al., 2021;Shaffer et al., 2014). Considered to be a healthy demonstration of cardiovascular function, high HRV is propagated by parasympathetic guide, while low HRV signifies sympathetic control (Kim et al., 2018;Papadakis et al., 2021;Rajendra Acharya et al., 2006;Sassi et al., 2015). In participating gamers, measurement of HRV was conducted before and after tournament play, producing significant differences in HR intervals between the measured periods substantiating esports gameplay as an inducer of physiological stress on competitors . ...
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Esports is a global competitive phenomenon in which gamers and spectating enthusiasts engage in virtual playing competition. The growth of esports has fostered so much prominence that the establishment of amateur intercollegiate competition has taken place across the United States. Numerous institutions have sanctioned esports teams and have welcomed players as student-athletes within athletics departments. Positioned as the recipient to a remarkable boom in global esteem, the appeal of esports has stimulated exponential growth in commercial value, patronage, and societal acceptance. Scholars have since assessed the entwining of the world of sport with competitive gaming, sparking debate arguing whether esports is an admissible form of sport, and its participants credited as athletes eligible for intercollegiate athletic scholarships. In this literature overview—perspective article—we present characteristics of mainstream allure, operational terminology, and fitness of participants to render the status of esports to be, or, not to be, a collegiate sport. It is this multidisciplinary point of analysis from which the authors conclusion is supplicated. While esports currently do not satisfy the philosophical principles from which sport is characterized, growing acceptance, economic value, and alternative exhibitions of athleticism suggests the prospective of the industry’s future embrace.
... Autonomic 'flexibility', or the ability to shift between high and low arousal states in response to the environment is correlated with higher HRV (Appelhans and Luecken 2006). HRV is thus widely accepted as a measure of autonomic balance and parasympathetic nervous system power (McCraty et al. 2009;Rajendra Acharya et al. 2006;Shaffer and Ginsberg 2017). Several HRV indices are thought to be correlated with parasympathetic nervous system activity (which may also be referred to as vagal tone or vagally mediated HRV)-and the time-domain measure RMSSD is often accepted as the best assessment of vagal tone (Laborde, Mosley, and Thayer 2017). ...
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... EDA is a property of the skin that indicates variations in electrical conduction in response to sweat secretions and is a pure sympathetic index (Klimek et al., 2023;Boucsein, 2012;Shields et al., 1987). HRV represents the variation in the time interval between heartbeats, a parasympathetic index reflecting vagal activity, and is measured by the root mean square of successive differences between beat intervals (Acharya et al., 2006;Umetani et al., 1998;Ernst, 2017). Participants wore the monitoring device on their non-dominant hands before and after forest bathing while performing the stressful MAT test. ...
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Climate change and urbanization increase the vulnerability of cities. The (re)integration of nature can mitigate these effects by enhancing well-being through mechanisms such as biophilia, attention restoration, stress reduction, and a sense of connectedness to nature. This thesis aims to confirm the effects of urban nature on well-being, explore the associated psychological processes, analyze psychophysiological impacts, and assess the influence of environmental specifics through three studies involving city dwellers. The first study, involving 479 participants, shows that perceived quality and quantity of urban nature increase positive affects, especially among those with a strong connectedness to nature. The second study, conducted with 104 participants using 360° virtual reality (VR) videos, reveals that more natural environments reduce cognitive effort, as indicated by eye movements. The third study, with 83 participants in VR, compares a standard urban environment to a nature-enriched version after a stressful task. It combines subjective and objective measures (heart rate variability and electrodermal conductance) of stress, highlighting the importance of the sense of presence to optimize the benefits of VR experiences. The results demonstrate that urban nature enhances well-being, although its effects on attention and stress are more nuanced. The discussion highlights the importance of nature in mitigating the impacts of climate change and the usefulness of VR in environmental psychology research.
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Daily stressors elicit physiological and mental responses impacting health, cognition, and behavior. Accurately assessing stress responses in natural settings remains challenging despite extensive research, though wrist-worn devices have the potential to address this gap through remote data collection. The Garmin fitness tracker provides a stress score largely based on HRV which must be validated prior to use in research. This study aimed to assess the stress score given by the Garmin Vivosmart 4 against HR and HRV from ECG recordings derived by the Polar H10 chest strap. A pilot study of 29 participants was conducted, followed by power calculations and preregistration of the main study which included 60 participants. Data were collected simultaneously from both devices during a laboratory session of restful and mental-stress-inducing tasks. Garmin's stress score, mean HR, SD2/SD1, and HF power exhibited significant differences between stress and rest conditions. Moreover, Garmin's stress score correlated significantly with HR, RMSSD, and SD2/SD1. Our findings suggest that physiological responses to mental stress were influenced by sex and tonic HRV. The study suggests that the GSS is indicative of mental stress, with its accessibility and noninvasive nature promising widespread utilization in various research domains.
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It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.
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A basic mechanism of domestication is the selection for fearlessness and acceptance of humans as social partners, which may affect risk-taking behavior and the ability to use humans as social support, both at the behavioural and physiological levels. We combined behavioural observations with heart rate parameters (i.e., HR and heart rate variability, HRV) in equally raised and housed wolves and dogs to assess the responses to food offered in the vicinity of a potential stressor (an unknown spinning object) with and without social support from a familiar human. Based on previous studies on neophobia in wolves and dogs, we expected dogs to be less scared of the object, approach more quickly, show less ambivalent behaviour, lower HR, and higher HRV, than wolves, especially at the presence of a human partner. However, we found that mainly age and the presence of a familiar human affected the behaviour of our subjects: older wolves and dogs were generally bolder and faster to approach the food and the familiar human’s presence increased the likelihood of taking it. HR rate parameters were affected by age and the stage of the test. Wolves and dogs showed particularly high HRs at the beginning and end of the test sessions. We conclude that in our paradigm, wolves’ and dogs’ risk-proneness varied with age, rather than species. Additionally, the presence of a familiar human increased the motivation of both, dogs and wolves to take the food.
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Context This article introduces a new cloud-based point-of-care system to monitor heart rate variability (HRV). Aims Medical investigations carried out at dispensaries or hospitals impose substantial physiological and psychological stress (white coat effect), disrupting cardiovascular homeostasis, which can be taken care by point-of-care cloud computing system to facilitate secure patient monitoring. Settings and Design The device employs MAX30102 sensor to collect peripheral pulse signal using photoplethysmography technique. The non-invasive design ensures patient compliance while delivering critical insights into Autonomic Nervous System activity. Preliminary validations indicate the system’s potential to enhance clinical outcomes by supporting timely, data-driven therapeutic adjustments based on HRV metrics. Subjects and Methods This article explores the system’s development, functionality, and reliability. System designed is validated with peripheral pulse analyzer (PPA), a research product of electronics division, Bhabha Atomic Research Centre. Statistical Analysis Used The output of developed HRV monitor (HRVM) is compared using Pearson’s correlation and Mann–Whitney U-test with output of PPA. Peak positions and spectrum values are validated using Pearson’s correlation, mean error, standard deviation (SD) of error, and range of error. HRV parameters such as total power, mean, peak amplitude, and power in very low frequency, low frequency, and high frequency bands are validated using Mann–Whitney U-test. Results Pearson’s correlation for spectrum values has been found to be more than 0.97 in all the subjects. Mean error, SD of error, and range of error are found to be in acceptable range. Conclusions Statistical results validate the new HRVM system against PPA for use in cloud computing and point-of-care testing.
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Background: Complex chronic conditions like Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome involve energy limitations and changes in heart rate variability (HRV) and resting heart rate (HR). Mobile health technologies now offer real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. Using a high-density dataset from an observational longitudinal study, we aimed to describe, quantify, and predict within-person co-variations in daily biometric data and subsequent crash, fatigue, and brain fog symptom occurrences. Methods: Leveraging data collected through a mobile health app (n=4,244), we developed predictive models using mixed-effects linear regression and logistic regression to explore how within-person fluctuations in biometrics (HR, HRV, and respiratory rate) predict dynamic change in symptomology (crash, fatigue, and brain fog). Predictive performance was assessed using 5-fold stratified cross-validation and compared to a 20% holdout set to evaluate model generalizability to new observations and individuals. Results: Across all symptom domains, within-person changes in HRV and HR consistently emerged as key predictors of symptom change across all models, with higher HR and lower HRV conferring risk for crashes, fatigue, and brain fog. Moreover, 7-day biometric stability (or variable dispersion) was a robust predictor of symptom occurrence and severity. Models trained solely on biometric features achieved moderate predictive performance in the stratified cross-validation set; however, incorporating random effects to capture individual-specific variations and prior-day symptom reports substantially enhanced model accuracy, with AUC values reaching .91. Discussion and Conclusion: This study is the first to use data-driven models to predict everyday symptom experiences in individuals with complex chronic illnesses based on biometric fluctuations. Findings demonstrate the potential utility of mobile health tools for real-time monitoring of symptoms and highlight the need for further research to refine these predictive models and integrate them into clinical decision-making processes.
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This study investigates changes in cardiorespiratory coupling during clinic breathing training and its impact on autonomic nervous functioning compared with heart rate variability (HRV). A total of 39 subjects undergoing dynamic electrocardiogram-recorded breathing training were analyzed. Subjects were divided into early- and late-training periods, and further categorized based on changes in HRV indexes. Subtypes were identified using time-frequency cardiorespiratory coupling diagrams. Significant differences were observed in the high-frequency (HF) index between training stages in the subgroup with increasing HF-HRV ( p = 0.0335). Both unimodal and bimodal subtypes showed significant high-frequency coupling (HFC) in the mid-training period compared with early and late stages (both p < 0.0001), suggesting improved parasympathetic cardiac regulation or reduced sympathetic control. This study highlights the potential of nonstationary cardiorespiratory coupling analysis alongside traditional HRV in evaluating the therapeutic effect of breathing training on autonomic nervous function. Cardiorespiratory coupling analysis could provide valuable adjunctive information to HRV measures for assessing the impact of breathing training.
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Heart rate variability (HRV), indicating the variation in intervals between consecutive heartbeats, is a crucial physiological indicator of human health. However, detecting HRV using frequency-modulated continuous-wave (FMCW) radar is highly susceptible to interference from respiration, minor body movements, and environmental noise, especially in multi-target scenarios. To address these challenges, we propose the Health-Radar system, which comprises three functional modules. In the target detection module, the system accurately identifies the number and locations of targets. In the phase extraction module, the signal undergoes DC offset calibration to extract the chest displacement signals. In the heartbeat signal extraction module, we introduce Health-VMD, an adaptive parameter variational mode decomposition (VMD) method. This method optimizes the VMD parameters using an improved grasshopper optimization algorithm (GOA) and accurately extracts vital sign signals from chest displacement signals to estimate HRV. Additionally, we propose a novel objective function, composed of permutation entropy, mutual information, and energy loss rate (PME), specifically designed for vital sign extraction. Experiments with multiple participants in various scenarios demonstrated that the designed system can accurately identify different targets and detect HRV with high precision. The root mean square error (RMSE) of the detected inter-beat intervals (IBI) is 29.72ms, the RMSE of the standard deviation of NN intervals (SDNN) is 4.1ms, and the RMSE of the root mean square of successive differences (RMSSD) is 18.61ms, outperforming existing methods.
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In 57 normal subjects (age 20-60 years), we analyzed the spontaneous beat-to-beat oscillation in R-R interval during control recumbent position, 90° upright tilt, controlled respiration (n = 16) and acute (n = 10) and chronic (n = 12) β-adrenergic receptor blockade. Automatic computer analysis provided the autoregressive power spectral density, as well as the number and relative power of the individual components. The power spectral density of R-R interval variability contained two major components in power, a high frequency at ~ 0.25 Hz and a low frequency at ~ 0.1 Hz, with a normalized low frequency: high frequency ratio of 3.6 ± 0.7. With tilt, the low-frequency component became largely predominant (90 ± 1%) with a low frequency: high frequency ratio of 21 ± 4. Acute β-adrenergic receptor blockade (0.2 mg/kg IV propranolol) increased variance at rest and markedly blunted the increase in low frequency and low frequency: high frequency ratio induced by tilt. Chronic β-adrenergic receptor blockade (0.6 mg/kg p.o. propranolol, t.i.d.), in addition, reduced low frequency and increased high frequency at rest, while limiting the low frequency: high frequency ratio increase produced by tilt. Controlled respiration produced at rest a marked increase in the high-frequency component, with a reduction of the low-frequency component and of the low frequency: high frequency ratio (0.7 ± 0.1); during tilt, the increase in the low frequency: high frequency ratio (8.3 ± 1.6) was significantly smaller. In seven additional subjects in whom direct high-fidelity arterial pressure was recorded, simultaneous R-R interval and arterial pressure variabilities were examined at rest and during tilt. Also, the power spectral density of arterial pressure variability contained two major components, with a relative low frequency: high frequency ratio at rest of 2.8 ± 0.7, which became 17 ± 5 with tilt. These power spectral density components were numerically similar to those observed in R-R variability. Thus, invasive and noninvasive studies provided similar results. More direct information on the role of cardiac sympathetic nerves on R-R and arterial pressure variabilities was derived from a group of experiments in conscious dogs before and after bilateral stellectomy. Under control conditions, high frequency was predominant and low frequency was very small or absent, owing to a predominant vagal tone. During a 9% decrease in arterial pressure obtained with IV nitroglycerin, there was a marked increase in low frequency, as a result of reflex sympathetic activation. Bilateral stellectomy prevented this low-frequency increase in R-R but not in arterial pressure autospectra, indicating that sympathetic nerves to the heart are instrumental in the genesis of low-frequency oscillations in R-R interval.
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A new graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples.
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We present the first algorithms that allow the estimation of non-negative Lyapunov exponents from an experimental time series. Lyapunov exponents, which provide a qualitative and quantitative characterization of dynamical behavior, are related to the exponentially fast divergence or convergence of nearby orbits in phase space. A system with one or more positive Lyapunov exponents is defined to be chaotic. Our method is rooted conceptually in a previously developed technique that could only be applied to analytically defined model systems: we monitor the long-term growth rate of small volume elements in an attractor. The method is tested on model systems with known Lyapunov spectra, and applied to data for the Belousov-Zhabotinskii reaction and Couette-Taylor flow.
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The study goal was to investigate autonomic activity with heart rate variability analysis during different sleep stages in males and females. The study utilized a 2 Groups (males, females) x 4 States (waking, stage 2 sleep, stage 4 sleep, rapid-eye movement sleep) mixed design with one repeated, within-subjects factor (i.e., state). The study was carried out in the sleep laboratory of the Thomas N. Lynn Institute for Healthcare Research. Twenty-four healthy adults (fourteen females and ten males). NA. All participants underwent polysomnographic monitoring and electrocardiogram recordings during pre-sleep waking and one night of sleep. Fifteen-minute segments of beat-to-beat heart rate intervals during waking, stage 2 sleep, stage 4 sleep, and REM sleep were subjected to spectral analysis. Compared to NREM sleep, REM sleep was associated with decreased high frequency (HF) band power, and significantly increased low frequency (LF) to (HF) ratio. Compared to females, males showed significantly elevated LF/HF ratio during REM sleep. Males also demonstrated significantly decreased HF band power during waking when compared to females. No significant sleep- or gender-related changes in LF band power were found. The results confirmed changes in autonomic activity from waking to sleep, with marked differences between NREM and REM sleep. These changes were primarily due to stage-related alterations in vagal tone. REM sleep was characterized by increased sympathetic dominance, secondary to vagal withdrawal. The data also suggested gender differences in autonomic functioning during waking and sleep, with decreased vagal tone during waking and increased sympathetic dominance during REM sleep in the males.
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What is most intriguing about heart rate (HR) variability is that there is so much of it. HR is constantly responding both rapidly and slowly to various physiological perturbations. We now understand that the frequency and amplitude of these HR fluctuations are indicative of the autonomic control systems underlying the response.
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Term healthy newborn infants underwent polygraphy between days 4 and 6 of life. Behavioral states were determined according to Prechtl's criteria. The neonatal heart rate was analyzed for the various behavioral states, with the use of quantitative indices for long-term and short-term irregularity. The applied indices were, respectively, the long-term irregularity index (LTI index) and the interval difference index (ID index). During state 1 the R-R interval length was longer (p < 0.01), the LTI index lower (p < 0.01), and the ID index higher (p < 0.02) than in the immediately preceding or following state 2. For nonconsecutive states 1 and 2 a maximum separation was obtained with the discriminant function 0.0159 RR − 0.065 LTI + 0.062 ID − 7.49. This discriminant function gave a total percentage of correct classification for states 1 and 2 epochs of 93%. The data are discussed with respect to the presence of cycling sleep states in the newborn infant as well as in the fetus. Prospects for fetal antepartum heart rate monitoring are considered.
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This article describes an approach to chaotic modeling in which a continuous model is developed based on a conjectured solution to the logistic equation. As a result of this approach, two practical methods for quantifying variability in data sets have been derived. The first is a graphical representation obtained by using second-order difference plots of time series data. The second is a central tendency measure (CTM) that quantifies this degree of variability. The CTM can then be used as a parameter in decision models, such as neural networks. It appears that measuring the degree of variability is a more useful measure of chaos, as demonstrated by the application of this work to the analysis of congestive heart failure patients as compared to normal controls.
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Spectral analysis of spontaneous heart rate fluctuations were assessed by use of autonomic blocking agents and changes in posture. Low-frequency fluctuations (below 0.12 Hz) in the supine position are mediated entirely by the parasympathetic nervous system. On standing, the low-frequency fluctuations increase and are jointly mediated by the sympathetic and parasympathetic nervous systems. High-frequency fluctuations, at the respiratory frequency, are decreased by standing and are mediated solely by the parasympathetic system. Heart rate spectral analysis is a powerful noninvasive tool for quantifying autonomic nervous system activity.
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Objective: There are conflicting results on the relationship between heart rate variability (HRV) and major depression. There is some research reporting decreased heart rate variability in depressed patients, which may result in increased cardiovascular mortality and morbidity. This study aims to investigate the HRV in a group of physically healthy depressed patients in comparison to healthy subjects. Method: Twenty-one depressed subjects were compared to same number of healthy controls on the measures of HRV as measured by Kardiosis DL 700 Digital tree channel recorder Holter monitors. The study group was also assessed with Hamilton anxiety and depression scales. The HRV measures were compared in between the two groups and correlations between levels of anxiety and depression with HRV measures were sought for. Results: There was no statistically significant difference between the study and control groups on the measures of HRV. No significant relationship between the levels of anxiety and depression and HRV measures were found. Conclusions: In physically healthy depressed adults HRV does not differ from healthy subjects. This means that depression does not pose an additional risk factor for cardiovascular disease in physically healthy adults. This finding gives support to some previous research which did not find any relationship between depression and heart rate variability.
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: Time-frequency localization is one of the most essential features of the wavelet transform. It was shown that while high order Daubechies and Battle-Lemarie wavelets give poor time-frequency localizations, the Chui-Wang spline-wavelets provide asymptotically optimal time-frequency windows. On the other hand, we also showed that by using the scale 3 instead of 2, symmetry can be achieved by orthonormal wavelets with compact support. Multivariate wavelets, particularly those with matrix dilation, were studied, and the theory of oversampling frames was extended to this setting. Interpolating wavelets have distributional duals that lead to the notion of functional wavelet transform. Other extensions required a study of the stability issue and algorithmic construction in multivariate splines. Applications to systems theory lead to the study of Hankel approximation and localization of neural networks. (AN)
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Proposes a new method based on phase space technique for the analysis of cardiovascular signals. The method is based on finding a measure which depends on the distribution of signals in phase space. Results obtained tend to support the feasibility of the proposed method in possibly detecting abnormal conditions in cardiovascular signals
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Interrelationship between age and heart rate, spectral parameters of heart rate variability, peak frequency of mid‐frequency heart rate fluctuations, respiratory rate and coherence coefficient was investigated in normotensive healthy volunteers. Thereafter these parameters were correlated with each other in a young subject group to eliminate the effect of age. The peak frequency of mid‐frequency heart rate fluctuations was significantly inversely related to age. The overall heart rate variability (0.01‐.05 Hz) was reduced with aging due to the diminution of power spectral densities in the mid‐frequency (0.05–0.15 Hz) and respiration related frequency band (heart rate spectral power around the mean respiratory rate). In the young subject group total power was positively related to the power of the discrete spectral components. The respiratory rate was inversely related to mid‐frequency and respiration related frequency components of heart rate variability. Our results appear to support on the one hand the hypothesis of age‐related alterations in the autonomic cardiovascular control mechanisms. On the other hand our data show significant correlations between spectral parameters of heart rate variability and respiration, which should be taken into consideration in the interpretation of heart rate power spectra in spontaneously breathing subjects.
Article
Under healthy conditions, the normal cardiac (sinus) interbeat interval fluctuates in a complex manner. Quantitative analysis using techniques adapted from statistical physics reveals the presence of long-range power-law correlations extending over thousands of heartbeats. This scale-invariant (fractal) behavior suggests that the regulatory system generating these fluctuations is operating far from equilibrium. In contrast, it is found that for subjects at high risk of sudden death (eg, congestive heart failure patients), these long-range correlations break down. Application of fractal scaling analysis and related techniques provides new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as motivating development of novel physiologic models of systems that appear to be heterodynamic rather than homeostatic
Article
A technique for the time-variant analysis of quadratic phase coupling (QPC) in heart rate data is introduced and tested in 6 human neonates during quiet sleep. The set up of the approach is based up on the assumption that QPCs in the heart rate variability (HRV) are related to amplitude modulation effects. The application of the biamplitude deals with the detection of the coupling pattern and the bicoherence is used for the statistical quantification of coupling. By means of the results of bispectral analysis the time-variant processing has been adapted. The frequency-selective complex demodulation of the HRV leads to the envelope of the respiratory sinus arrhythmia (RSA), this has been used as one input for a time-variant coherence analysis. The other input is the low-pass filtered 10-second-rhythm of the HRV. A time-continuous quantification of the QPC, caused by amplitude modulation (10-second-rhythm modulates the RSA), is possible using this approach. According to our observed results in neonatal HRV both a phase co-ordination between the 10-second-rhythm and RSA as well as a non-linear coupling (amplitude modulation) between these HRV components can be seen.
Article
From the Publisher:Biomedical / Electrical Engineering Nonlinear Biomedical Signal Processing Volume II: Dynamic Analysis and Modeling A volume in the IEEE Press Series on Biomedical Engineering Metin Akay, Series Editor Featuring current contributions by experts in signal processing and biomedical engineering, this book introduces the concepts, recent advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive coverage of nonlinear signal and image processing techniques. Nonlinear Biomedical Signal Processing: Volume II combines analytical and biological expertise in the original mathematical simulation and modeling of physiological systems. Detailed discussions of the analysis of steady-state and dynamic systems, discrete-time system theory, and discrete modeling of continuous-time systems are provided. Biomedical examples include the analysis of the respiratory control system, the dynamics of cardiac muscle and the cardiorespiratory function, and neural firing patterns in auditory and vision systems. Examples include relevant MATLAB(r) and Pascal programs. Topics covered include:*Nonlinear dynamics*Behavior and estimation*Modeling of biomedical signals and systems*Heart rate variability measures, models, and signal assessments*Origin of chaos in cardiovascular and gastric myoelectrical activity*Measurement of spatio-temporal dynamics of human epileptic seizures.A valuable reference book for medical researchers, medical faculty, and advanced graduate students, it is also essential reading for practicing biomedical engineers. Nonlinear Biomedical Signal Processing, Volume II is anexcellent companion to Dr. Akay's Nonlinear Biomedical Signal Processing, Volume I: Fuzzy Logic, Neural Networks, and New Algorithms.
Conference Paper
We investigate the prospect of using bicoherence features for blind image splicing detection. Image splicing is an essential operation for digital photomontaging, which in turn is a technique for creating image forgery. We examine the properties of bicoherence features on a data set, which contains image blocks of diverse image properties. We then demonstrate the limitation of the baseline bicoherence features for image splicing detection. Our investigation has led to two suggestions for improving the performance of bicoherence features, i.e., estimating the bicoherence features of the authentic counterpart and incorporating features that characterize the variance of the feature performance. The features derived from the suggestions are evaluated with support vector machine (SVM) classification and is shown to improve the image splicing detection accuracy from 62% to about 70%.
Article
Objectives: This study aimed to quantify the complex dynamics of beat-to-beat sinus rhythm heart rate fluctuations and to determine their differences as a function of gender and age. Background: Recently, measures of heart rate variability and the nonlinear "complexity" of heart rate dynamics have been used as indicators of cardiovascular health. Because women have lower cardiovascular risk and greater longevity than men, we postulated that there are important gender-related differences in beat-to-beat heart rate dynamics. Methods: We analyzed heart rate dynamics during 8-min segments of continuous electrocardiographic recording in healthy young (20 to 39 years old), middle-aged (40 to 64 years old) and elderly (65 to 90 years old) men (n = 40) and women (n = 27) while they performed spontaneous and metronomic (15 breaths/min) breathing. Relatively high (0.15 to 0.40 Hz) and low (0.01 to 0.15 Hz) frequency components of heart rate variability were computed using spectral analysis. The overall "complexity" of each heart rate time series was quantified by its approximate entropy, a measure of regularity derived from nonlinear dynamics ("chaos" theory). Results: Mean heart rate did not differ between the age groups or genders. High frequency heart rate power and the high/low frequency power ratio decreased with age in both men and women (p < 0.05). The high/low frequency power ratio during spontaneous and metronomic breathing was greater in women than men (p < 0.05). Heart rate approximate entropy decreased with age and was higher in women than men (p < 0.05). Conclusions: High frequency heart rate spectral power (associated with parasympathetic activity) and the overall complexity of heart rate dynamics are higher in women than men. These complementary findings indicate the need to account for gender-as well as age-related differences in heart rate dynamics. Whether these gender differences are related to lower cardiovascular disease risk and greater longevity in women requires further study.
Article
The short- and long-term effects of cigarette smoking on autonomic cardiac regulation were investigated by power spectral analysis of heart rate variability under controlled respiration (15/ min). The short-term effects were examined in 9 smokers without evidence of cardiopulmonary disorders after an overnight abstinence from smoking. The heart rate spectral component reflecting the respiratory sinus arrhythmia (0.25 Hz), a quantitative index of vagal cardiac control, decreased 3 minutes after smoking 1 cigarette (p = 0.0061) and the component reflecting Mayer wave sinus arrhythmia (0.04 to 0.15 Hz), which includes sympathetically mediated activity, increased after 10 to 17 minutes (p = 0.0124). The long-term effects were examined in 81 normal subjects comprising 25 nonsmokers, 31 moderate (1 to 24 cigarettes/ day) smokers and 25 heavy (>25 cigarettes/day) smokers after an overnight abstinence. Although the magnitude of the Mayer wave component was unaffected by the smoking status, the respiratory component in the supine position was smaller in the young (≤30 years) heavy smokers than in the young nonsmokers or moderate smokers (p = 0.0078). Also, postural changes in the components, a decrease in the respiratory component and an increase in the Mayer wave component with standing, were observed in the nonsmokers but not in the heavy smokers. These results suggest that smoking causes an acute and transient decrease in vagal cardiac control, and that heavy smoking causes long-term reduction in vagal cardiac control in young people and blunted postural responses in autonomic cardiac regulation.
Article
To investigate the influence of maturational and physiological factors on heart rate variability in spontaneously breathing very preterm infants (n = 29) a multiparametric study was performed during the first 3 days of life in infants born at a gestational age below 33 weeks. Four times a day, RR-intervals, respiration curve and rate, transcutaneously measured blood gases and observed body movements were recorded while the infants were asleep. All data were stored simultaneously in a micro-computer. Non-invasively measured blood pressure and patency of the ductus arteriosus were documented as well. Four sets of short- (STV) and long term variability (LTV) indices were calculated. Both STV and LTV appeared to be significantly influenced by conceptional and postnatal age in the appropriate for gestational age infants. LTV was influenced by the behavioural state and body movements. During state coincidence 2 (‘active sleep’) LTV was influenced by respiratory rate and the variations in transcutaneous PO2. An effect of blood pressure or ductus patency could not be demonstrated.
Article
The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.
Article
Detecting the presence of chaos in a dynamical system is an important problem that is solved by measuring the largest Lyapunov exponent. Lyapunov exponents quantify the exponential divergence of initially close state-space trajectories and estimate the amount of chaos in a system. We present a new method for calculating the largest Lyapunov exponent from an experimental time series. The method follows directly from the definition of the largest Lyapunov exponent and is accurate because it takes advantage of all the available data. We show that the algorithm is fast, easy to implement, and robust to changes in the following quantities: embedding dimension, size of data set, reconstruction delay, and noise level. Furthermore, one may use the algorithm to calculate simultaneously the correlation dimension. Thus, one sequence of computations will yield an estimate of both the level of chaos and the system complexity.
Article
We present a technique to measure the fractal dimension of the set of points (t, f(t)) forming the graph of a function f defined on the unit interval. First we apply it to a fractional Brownian function [1] which has a property of self-similarity for all scales, and we can get the stable and precise fractal dimension. This technique is also applied to the observational data of natural phenomena. It does not show self-similarity all over the scale but has a different self-similarity across the characteristic time scale. The present method gives us a stable characteristic time scale as well as the fractal dimension.
Article
We study the correlation exponent v introduced recently as a characteristic measure of strange attractors which allows one to distinguish between deterministic chaos and random noise. The exponent v is closely related to the fractal dimension and the information dimension, but its computation is considerably easier. Its usefulness in characterizing experimental data which stem from very high dimensional systems is stressed. Algorithms for extracting v from the time series of a single variable are proposed. The relations between the various measures of strange attractors and between them and the Lyapunov exponents are discussed. It is shown that the conjecture of Kaplan and Yorke for the dimension gives an upper bound for v. Various examples of finite and infinite dimensional systems are treated, both numerically and analytically.
Article
We describe a statistical approach for identifying nonlinearity in time series. The method first specifies some linear process as a null hypothesis, then generates surrogate data sets which are consistent with this null hypothesis, and finally computes a discriminating statistic for the original and for each of the surrogate data sets. If the value computed for the original data is significantly different than the ensemble of values computed for the surrogate data, then the null hypothesis is rejected and nonlinearity is detected. We discuss various null hypotheses and discriminating statistics. The method is demonstrated for numerical data generated by known chaotic systems, and applied to a number of experimental time series which arise in the measurement of superfluids, brain waves, and sunspots; we evaluate the statistical significance of the evidence for nonlinear structure in each case, and illustrate aspects of the data which this approach identifies.
Article
We describe a statistical approach for identifying nonlinearity in time series. The method first specifies some linear process as a null hypothesis, then generates surrogate data sets which are consistent with this null hypothesis, and finally computes a discriminating statistic for the original and for each of the surrogate data sets. If the value computed for the original data is significantly different than the ensemble of values computed for the surrogate data, then the null hypothesis is rejected and nonlinearity is detected. We discuss various null hypotheses and discriminating statistics. The method is demonstrated for numerical data generated by known chaotic systems, and applied to a number of experimental time series which arise in the measurement of superfluids, brain waves, and sunspots; we evaluate the statistical significance of the evidence for nonlinear structure in each case, and illustrate aspects of the data which this approach identifies.
Article
Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. Time and frequency domain heart rate (HR) variability measures, along with short- and long-term correlation (fractal) properties of R-R intervals (exponents alpha(1) and alpha(2)) and power-law scaling of the power spectra (exponent beta), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction </=35%). During a mean+/-SD follow-up period of 685+/-360 days, 114 patients died (25.6%), with 75 deaths classified as arrhythmic (17.0%) and 28 as nonarrhythmic (6.3%) cardiac deaths. Several traditional and fractal measures of R-R interval variability were significant univariate predictors of all-cause mortality. Reduced short-term scaling exponent alpha(1) was the most powerful R-R interval variability measure as a predictor of all-cause mortality (alpha(1) <0.75, relative risk 3.0, 95% confidence interval 2.5 to 4.2, P<0.001). It remained an independent predictor of death (P<0.001) after adjustment for other postinfarction risk markers, such as age, ejection fraction, NYHA class, and medication. Reduced alpha(1) predicted both arrhythmic death (P<0.001) and nonarrhythmic cardiac death (P<0.001). Analysis of the fractal characteristics of short-term R-R interval dynamics yields more powerful prognostic information than the traditional measures of HR variability among patients with depressed left ventricular function after an acute myocardial infarction.
Article
The autonomic nervous system exerts a modulating effect on the risk of sudden cardiac death (SCD) in the setting of ischemic heart disease. The mechanism by which sympathetic tone increases the risk of ventricular arrhythmias is not known, though regional sympathetic denervation at and apical to the site of transmural infarction may result in regional supersensitivity to circulating catecholamines and play a role in ventricular arrhythmogenesis. [(123)I]MIBG scintigraphy enables noninvasive determination of regional cardiac denervation and may be a useful tool for probing the role of sympathetic nervous system in SCD. Increased vagal tone is generally protective against SCD. Newer tests such as baroreflex slope testing and various techniques for determination of heart rate variability, which provide indices of vagal tone, may have greater predictive value and are powerful tools in assessing the role of autonomic nervous system in SCD.
Article
Standard real business cycle models must rely on total factor productivity (TFP) shocks to explain the observed comovement of consumption, investment, and hours worked. This paper shows that a neoclassical model consistent with observed heterogeneity in labor supply and consumption can generate comovement in the absence of TFP shocks. Intertemporal substitution of goods and leisure induces comovement over the business cycle through heterogeneity in the consumption behavior of employed and unemployed workers. This result owes to two model features introduced to capture important characteristics of U.S. labor market data. First, individual consumption is affected by the number of hours worked: Employed agents consume more on average than the unemployed do. Second, changes in the employment rate, a central factor explaining variation in total hours, affect aggregate consumption. Demand shocks--such as shifts in the marginal efficiency of investment, as well as government spending shocks and news shocks--are shown to generate economic fluctuations consistent with observed business cycles.
Article
Heart rate variability refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability is important because it provides a window to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Parameters are extracted from the heart rate signals and analysed using computers for diagnostics. This paper describes the analysis of normal and seven types of cardiac abnormal signals using approximate entropy (ApEn), sample entropy (SampEn), recurrence plots and Poincare plot patterns. Ranges of these parameters for various cardiac abnormalities are presented with an accuracy of more than 95%. Among the two entropies, ApEn showed better performance for all the cardiac abnormalities. Typical Poincare and recurrence plots are shown for various cardiac abnormalities.
Article
To test whether or not the characteristics of the adult heart-rate reflect the condition of the central nervous system (as they seem to do in the fetus), ten patients with neurological deficits of acute onset were studied. No patients had received drugs and none was hypoxic. The findings indicate that the normal cyclic changes in heart-rate are reduced in the presence of severe brain damage. Variability decreases rapidly if intracranial pressure rises, and the rate of return of variability reflects the subsequent state of neuronal function, even when intracranial pressure has been restored to normal. In this limited setting, then, it appears that heart-rate variability may reflect the functional state of the central nervous system.
Article
The degree of parasympathetic heart rate control, PC, was defined as the decrease in average heart period (RR interval) caused by the elimination of parasympathetically mediated influences on the heart while keeping sympathetic activity unchanged. By reviewing published results on the interaction of sympathetic and parasympathetic heart rate control, the prediction was made that PC should be directly proportional to VHP, the peak-to-peak variations in heart period caused by spontaneous respiration. In sevel chloralose/urethan-anesthetized dogs the vagi were reversibly blocked by cooling, and PC (the difference between average heart period before and after cooling) and VHP (without cooling) were determined under a variety of conditions that included a) increasing vagal activity by elevating the blood pressure b) sympathetic blockade, and c) parasympathetic blockade. The relationship between VHP and PC was linear with an average correlation coefficient of 0.969 +/- 0.024 (SD) and a PC-axis intercept of 15.2 +/- 25.9 ms. In each dog the correlation coefficient between VHP and PC was higher than between VHP and the average heart period (avg correlation coef: 0.914 +/- 0.044). These results suggest that the degree of respiratory sinus arrhythmia may be used as a noninvasive indicator of the degree of parasympathetic cardiac control.
Article
The cardiovascular responses to the Valsalva manoeuvre and sustained handgrip were measured in 26 patients with chronic renal failure treated with intermittent haemodialysis. Twelve (50%) had an abnormal Valsalva response and ten (45%) had an abnormal handgrip response. There was a reduction in the beat-to-beat variation of heart rate at rest in those patients who had abnormal Valsalva manoeuvres, independent of age or the resting heart rate. It is concluded that autonomic nerve fibres may be damaged in patients with chronic renal failure on intermittent haemodialysis in the absence of symptoms of autonomic neuropathy. These studies suggest three simple ways of testing autonomic function in haemodialysis patients which could routinely be performed to detect patients at risk of developing an abnormal reaction to volume depletion during haemodialysis.
Article
Diminished heart rate variability is associated with high sympathetic tone and an increased mortality rate in heart failure cases. We constructed Poincaré plots of each sinus R-R interval plotted against the subsequent R-R interval from 24-hour Holter recordings of 24 healthy subjects (control group) and 24 patients with heart failure. Every subject in the control group had a comet-shaped Poincaré plot resulting from an increase in beat-to-beat dispersion as heart rate slowed. No patient with heart failure had this comet-shaped pattern. Instead, three distinctive patterns were identified: (1) a torpedo-shaped pattern resulting from low R-R interval dispersion over the entire range of heart rates, (2) a fanshaped pattern resulting from restriction of overall R-R interval ranges with enhanced dispersion, and (3) complex patterns with clusters of points characteristic of stepwise changes in R-R intervals. Poincaré pattern could not be predicted from standard deviations of R-R intervals. This first use of Poincaré plots in heart rate variability analysis reveals a complexity not readily perceived from standard deviation information. Further study is warranted to determine if this method will allow refined assessment of cardiac-autonomic integrity in heart failure, which could help identify patients at highest risk for sudden death.
Article
The analysis of the autonomic control of the heart, by means of indirect markers, may represent a new approach for identifying patients at higher risk for sudden cardiac death after a myocardial infarction. This possibility is based on the evidence that autonomic responses during acute myocardial ischemia are a major determinant of the outcome (i.e., occurrence of ventricular fibrillation or survival). Specifically, sympathetic activation can trigger malignant arrhythmias, whereas vagal activity may exert a protective effect. Several experimental observations have provided new insights on the relation between sympatho-vagal interactions and the likelihood for the occurrence of ventricular fibrillation. In an established experimental model for sudden death involving conscious dogs with a healed myocardial infarction, either depressed reflex chronotropic responses during a blood pressure rise or reduced variability of heart rate (respectively, markers of reflex and tonic cardiac vagal activity) identify dogs at greater risk to develop malignant arrhythmias during a new ischemic episode. In anesthetized cats, direct neural recording of vagal activity to the heart confirmed that vigorous reflex vagal activation during acute myocardial ischemia is associated with protection from ventricular fibrillation. Furthermore, in these experiments the reflex neural response to acute myocardial ischemia was predicted by the analysis of baroreflex sensitivity. The antifibrillatory effect of vagal activation is confirmed by the prevention of ventricular fibrillation during acute ischemia in dogs susceptible to sudden cardiac death by direct electrical stimulation of the right vagus. The clinical counterpart of these experimental data lies in three separate prospective studies showing a higher cardiac mortality in patients who after a myocardial infarction have a depressed baroreflex sensitivity or a decreased heart rate variability. A definitive answer on the role that the analysis of markers of cardiac vagal activity may play in risk stratification of patients with coronary artery disease should be provided by Autonomic Tone and Reflexes After Myocardial Infarction (ATRAMI), an ongoing prospective study. In ATRAMI, baroreflex sensitivity and heart rate variability will be assessed within 20 days after a myocardial infarction in 1,200 patients enrolled in Europe, U.S.A., and Japan with a minimum follow up of one year.