Figure 6 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
![Working principle of PPG sensors [19].](publication/325675749/figure/fig6/AS:636509350793216@1528767063587/Working-principle-of-PPG-sensors-19.png)
Source publication
![Figure 4. Different measurement points of PTT [45].](publication/325675749/figure/fig4/AS:636509350801408@1528767063356/Different-measurement-points-of-PTT-45_Q320.jpg)
Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medi...
Context in source publication
Context 1
... working principle of the PPG sensor is based on the emission of infrared light by an LED which penetrates the skin and blood vessels. This light is captured by the detector to measure the blood stream, as can be observed in Figure 6. The results of the PPG signal depend primarily on the flow of blood and oxygen to the capillary vessels in each heartbeat [19]. Theoretically, the PPG signal is formed by two components: (1) the DC offset, which represents the constant absorption of light passing through the tissues; and (2) the AC component generated by heartbeats affecting blood volume when light traverses the artery ...
Citations
... Graphical explanation of PTT calculation when using the PW foot as the landmark that indicates arrival of the PW. PTT pulse transit time, ECG electrocardiogram, PPG photoplethysmography [33]. Cluster Architecture With Docker. ...
Cardiovascular diseases (CVDs) constitute a substantial global health challenge, with heart diseases ranking among the leading causes of mortality worldwide. This paper addresses this urgent concern by proposing innovative approaches. The fusion of Digital Twin technology with artificial intelligence offers a unique framework for personalized diagnosis, therapy selection, remote monitoring, and real-time treatment adjustments. By combining virtual patient replicas with medical history, real-time data, and machine learning algorithms, the potential for early detection and prevention of heart diseases becomes a reality. This paper presents a comprehensive exploration of leveraging Digital Twin technology for precise and real-time heart disease prediction, focusing on data management, security, and preprocessing. The research aims to lay a robust foundation for the development of a medical decision support system capable of precise predictions and interventions within the realm of heart disease.
By combining virtual patient replicas with medical history, real-time data, and advanced machine learning algorithms, our paper explores the potential for early detection and prevention of heart diseases, centering on the development and detailed analysis of an ECG model. This ECG model leverages Digital Twin technology to enable precise and real-time heart disease prediction.
... In the last decade, the diagnosis and preliminary clinical analysis of an individual's state of health have been supported by non-invasive methods of monitoring biological signals [19]. The PPG signal deserves special attention, given its easy acquisition with affordable devices and the amount of physiological information it contains [20]. ...
Stress is one of the primary triggers of serious pathologies (e.g., depression, obesity, heart attack). Prolonged exposure to it can lead to addictive substance consumption and even suicide, without ignoring other adverse side effects in the economic, work and family spheres. Early detection of stress would relax the pressure of medical practice exercised by the population affected and result in a healthier society with a more satisfying quality of life. In this work, a convolutional-neural-network (CNN) model is proposed to detect an individual’s stress state by analyzing the diffusive dynamics of the photoplethysmographic (PPG) signal. The characteristic (p,q)-planes of the 0–1 test serve as a framework to preprocess the PPG signals and feed the CNN with the dynamic information they supply to typify an individual’s stress level. The methodology follows CRISP-DM (Cross Industry Standard Process for Data Mining), which provides the typical steps in developing data-mining models. An adaptation of CRIPS-DM is applied, adding specific transitions between the usual stages of deep-learning models. The result is a CNN model whose performance amounts to 97% accuracy in diagnosing the stress level; it compares with other published results.
... The Principle of PPG Sensor[21] ...
... The AC component reflects changes in blood volume synchronized with cardiac cycles. In contrast, the DC component represents the baseline blood volume level, affected by non-pulsatile tissue absorption and low-frequency variations associated with respiration, thermoregulation, and sympathetic activity [2,3]. PPG measurements require a light source (emitter) and a photo-detector (receiver) to measure blood volume changes at specific wavelengths. ...
Photoplethysmography (PPG) is widely used to assess cardiovascular health. However, its usage and standardization are limited by the impact of variable contact force and temperature, which influence the accuracy and reliability of the measurements. Although some studies have evaluated the impact of these phenomena on signal amplitude, there is still a lack of knowledge about how these perturbations can distort the signal morphology, especially for multi-wavelength PPG (MW-PPG) measurements. This work presents a modular multi-parametric sensor system that integrates continuous and real-time acquisition of MW-PPG, contact force, and temperature signals. The implemented design solution allows for a comprehensive characterization of the effects of the variations in these phenomena on the contour of the MW-PPG signal. Furthermore, a dynamic DC cancellation circuitry was implemented to improve measurement resolution and obtain high-quality raw multi-parametric data. The accuracy of the MW-PPG signal acquisition was assessed using a synthesized reference PPG optical signal. The performance of the contact force and temperature sensors was evaluated as well. To determine the overall quality of the multi-parametric measurement, an in vivo measurement on the index finger of a volunteer was performed. The results indicate a high precision and accuracy in the measurements, wherein the capacity of the system to obtain high-resolution and low-distortion MW-PPG signals is highlighted. These findings will contribute to developing new signal-processing approaches, advancing the accuracy and robustness of PPG-based systems, and bridging existing gaps in the literature.
... The assessment's findings demonstrate that the error compensation method enhances the precision of HRV analysis in both the time and frequency domains as well as in nonlinear analysis. When compared to recent ECG observing systems, PPG signal measurements are more accessible and require less hardware for signal acquisition [12]. PPG does not require a reference signal, making it possible to integrate PPG sensors with wristbands. ...
Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques were used to improve the performance of classifiers for the detection of cardiovascular disease (CVD) from PPG signals. PPG signals occupy a large amount of memory and, hence, the signals were dimensionally reduced in the initial stage. A total of 41 subjects from the Capno database were analyzed in this study, including 20 CVD cases and 21 normal subjects. PPG signals are sampled at 200 samples per second. Therefore, 144,000 samples per patient are available. Now, a one-second-long PPG signal is considered a segment. There are 720 PPG segments per patient. For a total of 41 subjects, 29,520 segments of PPG signals are analyzed in this study. Five dimensionality reduction techniques, such as heuristic- (ABC-PSO, cuckoo clusters, and dragonfly clusters) and transformation-based techniques (Hilbert transform and nonlinear regression) were used in this research. Twelve different classifiers, such as PCA, EM, logistic regression, GMM, BLDC, firefly clusters, harmonic search, detrend fluctuation analysis, PAC Bayesian learning, KNN-PAC Bayesian, softmax discriminant classifier, and detrend with SDC were utilized to detect CVD from dimensionally reduced PPG signals. The performance of the classifiers was assessed based on their metrics, such as accuracy, performance index, error rate, and a good detection rate. The Hilbert transform techniques with the harmonic search classifier outperformed all other classifiers, with an accuracy of 98.31% and a good detection rate of 96.55%.
... From the obtained rPPG signal, two of the most important vital signs, namely the heart rate (HR) and the respiratory rate (RR), can be calculated (Elliott and Coventry 2012). Both are important biomarkers for the prevention and diagnostics of various illnesses (Moraes et al. 2018). For example, the HR is a measure of physiological activity and has the ability to indicate a person's state of health (Fel and Malik 1994). ...
The selection of a suitable region of interest (ROI) is of great importance in camera-based vital signs estimation, as it represents the first step in the processing pipeline. Since all further processing relies on the quality of the signal extracted from the ROI, the tracking of this area is decisive for the performance of the overall algorithm. To overcome the limitations of classical approaches for the ROI, such as partial occlusions or illumination variations, a custom neural network for pixel-precise face segmentation called FaSeNet was developed. It achieves better segmentation results on two datasets compared to state-of-the-art architectures while maintaining high execution efficiency. Furthermore, the Matthews Correlation Coefficient was proposed as a loss function providing a better fitting of the network weights than commonly applied losses in the field of multi-class segmentation. In an extensive evaluation with a variety of algorithms for vital signs estimation, our FaSeNet was able to achieve better results in both heart and respiratory rate estimation. Thus, a ROI for vital signs estimation could be created that is superior to other approaches.
... The use and analysis of the PPG signal in this study may have provided further insight. In the present study, we used linear time domainbased HRV analysis; other investigators have utilized nonlinear HRV analysis methods, based on chaos theory such as analysis of trend fluctuations, correlation function, exponent of Hurst, fractal dimension, and the exponent of Lyapunov; these have also been reported using additional PPG [81]. These methods may prove to be useful in further research on HRV. ...
Background:
Diabetes mellitus has reached global epidemic proportions, with type 2 diabetes (T2DM) comprising more than 90% of all subjects with diabetes. Cardiovascular autonomic neuropathy (CAN) frequently occurs in T2DM. Heart rate variability (HRV) reflects a neural balance between the sympathetic and parasympathetic autonomic nervous systems (ANS) and a marker of CAN. Reduced HRV has been shown in T2DM and improved by physical activity and exercise. External addition of pulses to the circulation, as accomplished by a passive simulated jogging device (JD), restores HRV in nondiseased sedentary subjects after a single session. We hypothesized that application of JD for a longer period (7 days) might improve HRV in T2DM participants.
Methods:
We performed a nonrandomized study on ten T2DM subjects (age range 44-73 yrs) who were recruited and asked to use a physical activity intervention, a passive simulated jogging device (JD) for 7 days. JD moves the feet in a repetitive and alternating manner; the upward movement of the pedal is followed by a downward movement of the forefoot tapping against a semirigid bumper to simulate the tapping of feet against the ground during jogging. Heart rate variability (HRV) analysis was performed using an electrocardiogram in each subject in seated posture on day 1 (baseline, BL), after seven days of JD (JD7), and seven days after discontinuation of JD (Post-JD). Time domain variables were computed, viz., standard deviation of all normal RR intervals (SDNN), standard deviation of the delta of all RR intervals (SDΔNN), and the square root of the mean of the sum of the squares of differences between adjacent RR intervals (RMSSD). Frequency domain measures were determined using a standard Fast Fourier spectral analysis, as well as the parameters of the Poincaré plots (SD1 and SD2).
Results:
Seven days of JD significantly increased SDNN, SDΔNN, RMSSD, and both SD1 and SD2 from baseline values. The latter parameters remained increased Post-JD. JD did not modify the frequency domain measures of HRV.
Conclusion:
A passive simulated jogging device increased the time domain and Poincaré variables of HRV in T2DM. This intervention provided effortless physical activity as a novel method to harness the beneficial effects of passive physical activity for improving HRV in T2DM subjects.
... Many parameters that affect transmissive PPG (tPPG) in thin body parts, such as the fingers and the ear lobe, have been explained in the literature and, therefore, numerous devices based on tPPG are widely adopted in clinical practice [2,3]. Light absorption in the blood increases with increasing blood volume in the illuminated tissue and, hence, the pulsatile (AC) component of PPG is generated [4]. ...
... For the blood-volume-related part, we start from light transmission in blood, approximated by a diffusion equation with the assumption of small angle scattering [29]. This approach corresponds to the currently accepted theory that PPG depends on the blood volume in the illuminated tissue [2,3,30] and the varying optical properties of blood due to RBC aggregation and orientation [23]. Consequently, irradiance can be related to pressure as: ...
Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomechanical processes that influence the reflective PPG signal. We developed a theoretical model to describe the dependence of reflected light on the pressure, flow rate, and the hemorheological properties of erythrocytes. To verify the theory, we designed a silicone model of a human radial artery, inserted it in a mock circulatory circuit filled with porcine blood, and imposed static and pulsatile flow conditions. We found a positive, linear relationship between the pressure and the PPG and a negative, non-linear relationship, of comparable magnitude, between the flow and the PPG. Additionally, we quantified the effects of the erythrocyte disorientation and aggregation. The theoretical model based on pressure and flow rate yielded more accurate predictions, compared to the model using pressure alone. Our results indicate that the PPG waveform is not a suitable surrogate for intraluminal pressure and that flow rate significantly affects PPG. Further validation of the proposed methodology in vivo could enable the non-invasive estimation of arterial pressure from PPG and increase the accuracy of health-monitoring devices.
... Image encryption algorithms are related to plaintext, and combine two diffusion operations and a transform related to plaintext to encrypt the image and use a hyperchaotic system to generate the key stream [12,13]. In order to obtain an extremely grounded medical services research [14], framework [15,16] undertaken earlier sought to improve the security of medical data transfer. The study's objective is to use the best private and public key-based security possible for IoT-based therapeutic images to find the perfect key. ...
... It is anticipated that a random breed solution has a lower probability of being near the total optimum layout than an opposing breed solution. This approach uses the following conditions to create (15). ...
The expansion of the Internet of Things is expected to lead to the emergence of the Internet of Medical Things (IoMT), which will revolutionize the health-care industry (IoT). The Internet of Things (IoT) revolution is outpacing current human services thanks to its bright mechanical, economical, and social future. Security is essential because most patient information is housed on a cloud platform in the hospital. The security of medical images in the Internet of Things was investigated in this research using a new cryptographic model and optimization approaches. For the effective storage and safe transfer of patient data along with medical images, a separate framework is required. The key management and optimization will be chosen utilizing the Rivest–Shamir–Adleman-based Arnold map (RSA-AM), hostile orchestration (HO), and obstruction bloom breeding optimization (OBBO) to increase the encryption and decryption processes’ level of security. The effectiveness of the suggested strategy is measured using peak signal-to-noise ratio (PSNR), entropy, mean square error (MSE), bit error rate (BER), structural similarity index (SSI), and correlation coefficient (CC). The investigation shows that the recommended approach provides greater security than other current systems.
... HRV can be evaluated with different methods. Among the most common, there are the analysis in the frequency domain, or analysis of the power spectral density, and the analysis in the time domain (Cacioppo et al., 2010;Moraes et al., 2018;Valderrama et al., 2010; Figure 6). ...
... Thus, in conclusion, the analysis in the frequency domain and the analysis in the time domain are the most common methods in the field of applied psychophysiology in clinical and sports fields, as indicated by Cacioppo et al. (2010), Valderrama et al. (2010), and Moraes et al. (2018). However, it is important to mention that a recent line of research is starting to apply nonlinear measures of HRV that provide new opportunities to monitor cardiac autonomic regulation (we refer directly to the systematic review of Gronwald and Hoos (2020) for an in-depth analysis). ...
Stress is a psychophysical condition that causes an impairment in athletes' performance by causing an increase in sympathetic activity and an autonomic imbalance. The current methods for the measurement of psychophysiological stress introduce the use of the heart rate variability as a useful index of the well-being of these people. The heart rate variability corresponds to the time intervals between consecutive heartbeats, such as an irregularity in the normal sinus heart rhythm whose variability is due to the control exercised by a complex system of mechanisms, including the respiratory control system, and provides information about the activity of the sympathetic and parasympathetic branches of the autonomic nervous system. This review aims at summarizing the promising results, despite small amount, of the recent literature on the efficacy of heart rate variability biofeedback on the autonomic imbalance and psychophysical well-being of athletes as well as cognitive and motor performance.
