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Pressure-based Detection of Heart and Respiratory Rates from Human Body Surface using a Biodegradable Piezoelectric Sensor
Abstract
This study investigates the relationship between respiration and autonomic nervous system (ANS) activity and proposes a parallel detection method that can simultaneously extract the heart rate (HR) and respiration rate (RR) from different pulse waves measured using a novel biodegradable piezoelectric sensor. The synchronous changes in heart rate variability and respiration reveal the interaction between respiration and the cardiovascular system and their interconnection with ANS activity. Following this principle, respiration was extracted from the HR calculated beat-by-beat from pulse waves. Pulse waves were measured using multiple biodegradable piezoelectric sensors each attached to the human body surface. The Valsalva maneuver experiment was conducted on seven healthy young adults, and the extracted respiratory wave was compared with a reference respiratory wave measured simultaneously. The experimental results are consistent with the observations from reference waves, where R2 = 0.9506, p < 0.001 for the extracted RR and the reference RR, thus demonstrating the detection capability under different respiratory statuses.
... This paves the way for more accurate respiratory monitoring and enriches the options available for robust respiratory monitoring in telehealth applications. A preliminary version of this work has been reported in [32]. ...
... 2) Biodegradable Piezoelectric Sensor: We have applied a novel biodegradable piezoelectric sensor to estimate RR from HR generated by different pulse waves due to its flexibility [32]. This flexible piezoelectric film of poly(L-lactic acid) (PLLA) can sensitively respond to the pressure fluctuation caused by the pulse wave, and its large piezoelectric constant and low dielectric constant can convert the deformation into electricity more efficiently [30], [33]. ...
Respiration can modulate the cardiovascular system through the autonomic nervous system (ANS), deriving numerous methods for monitoring respiration based on cardiovascular biomarkers. However, the sensitivity of the ANS to environmental changes can negatively affect these methods, which suggests the necessity to evaluate their performance in estimating respiratory rate (RR). This paper aims to propose a method for robust estimation of RR using a biodegradable piezoelectric sensor by analyzing the robustness differences of these biomarkers under pain stimulation. In an electrocutaneous stimulus experiment conducted with 15 participants, arterial pulse waves near the elbow and wrist were measured, as well as the electrocardiogram and fingertip photoplethysmogram. The robustness of six biomarkers was quantified using respiratory quality index (RQI) and mean absolute percentage error (MAPE). Heart rate derived from the arterial pulse wave near the elbow achieves the best robustness (
RQI
$=85.67$
±12.84 %,
MAPE
$=2.22$
±1.81 %) of all biomarkers, whereas pulse wave velocity (PWV) from the elbow to the wrist performs best (
RQI
$=70.39$
±12.15 %,
MAPE
$=3.47$
±1.69 %) of the three biomarkers of PWV. Therefore, the robustness of biomarkers varies, as does the same biomarker measured at different sites. Our results reveal the heterogeneity of respiratory modulation on the cardiovascular system and demonstrate the robustness of the biomarkers of the arterial pulse wave near the elbow in estimating RR. This study can help smart wearables perfect respiratory monitoring and contribute a robust method for respiratory monitoring using a biodegradable piezoelectric sensor.
In this study, a personal acoustic comfort prediction model (PACPM) for exploring the acoustic comfort of oceanauts in a deep-sea manned submersible cabin was proposed, and an oceanauts’ task performance model (OTPM) was constructed in this study. Based on oceanauts’ comfort and task performance, the change characteristics in six different pure-noise environments (the sound pressure levels of the noise audio are 40dB (A), 45dB (A), 50dB (A), 55dB (A), 60dB (A), and 65dB (A) respectively) were analyzed. An effective method for improving acoustic comfort was proposed. According to the analysis, personal comfort at 40 and 45 dB(A) was higher than that at other noise levels. The oceanaut’s comfort and task performance of normal-weight people were significantly higher than those of thin people. Meanwhile, a comprehensive consideration of the demographic characteristics and physiological responses can effectively improve the prediction accuracy of the personnel acoustic comfort. Furthermore, the 45 dB (A) pure-noise environment overlaid with 40 dB(A) fast-paced light music effectively improves oceanauts’ comfort.
Practitioner Summary: This study provides a convenient and available method for analysing acoustic comfort in the cabins of deep-sea manned submersibles, including a quantitative prediction model and an effective method for sound environment improvements. These can be used to improve the comfort, task performance, and working efficiency of manned submersibles.
Key points:
Slow breathing practices have gained popularity in the western world due to their claimed health benefits, yet remain relatively untouched by the medical community.Investigations into the physiological effects of slow breathing have uncovered significant effects on the respiratory, cardiovascular, cardiorespiratory and autonomic nervous systems.Key findings include effects on respiratory muscle activity, ventilation efficiency, chemoreflex and baroreflex sensitivity, heart rate variability, blood flow dynamics, respiratory sinus arrhythmia, cardiorespiratory coupling, and sympathovagal balance.There appears to be potential for use of controlled slow breathing techniques as a means of optimising physiological parameters that appear to be associated with health and longevity, and that may extend to disease states; however, there is a dire need for further research into the area.
Educational aims:
To provide a comprehensive overview of normal human respiratory physiology and the documented effects of slow breathing in healthy humans.To review and discuss the evidence and hypotheses regarding the mechanisms underlying slow breathing physiological effects in humans.To provide a definition of slow breathing and what may constitute "autonomically optimised respiration".To open discussion on the potential clinical implications of slow breathing techniques and the need for further research.
A method for deriving respiration from the pulse photoplethysmographic (PPG) signal is presented. This method is based on the pulse width variability (PWV), and it exploits the respiratory information present in the pulse wave velocity and dispersion. It allows to estimate respiration signal from only a pulse oximeter which is a cheap and comfortable sensor. Evaluation is performed over a database containing electrocardiogram (ECG), blood pressure (BP), PPG, and respiratory signals simultaneously recorded in 17 subjects during a tilt table test. Respiratory rate estimation error is computed obtaining of 1.27 ± 7.81 % (0.14 ± 14.78 mHz). For comparison purposes, we have also obtained a respiratory rate estimation from other known methods which involve ECG, BP, or also PPG signals. In addition, we have also combined respiratory information derived from different methods which involve only PPG signal, obtaining a respiratory rate error of -0.17 ± 6.67 % (-2.16 ± 12.69 mHz). The presented methods, PWV and combination of PPG derived respiration methods, avoid the need of ECG to derive respiration without degradation of the obtained estimates, so it is possible to have reliable respiration rate estimates from just the PPG signal.
An erratum for this article can be found under doi: 10.1007/s11517-012-0997-2
The photoplethysmogram (PPG) is the pulsatile wave-form produced by the pulse oximeter, which is widely used for monitoring arterial oxygen saturation in patients. Various methods for extracting the breathing rate from the PPG waveform have been compared using a consistent data set, and a novel technique using autoregressive modelling is presented. This novel technique is shown to outperform the existing techniques, with a mean error in breathing rate of 0.04 breaths per minute.
Respiratory monitoring is crucial for the assessment of human health, and can be realized by detecting the humidity of exhaled gases. In this work, a facile method for the in situ fabrication of stable humidity sensors based on gel polymer electrolytes was developed. The obtained humidity sensors demonstrate ultrafast response/recovery times (0.19/0.30 s), which are very promising for monitoring respiration. Reliable signals for different respiratory states were obtained via humidity sensors. In addition, the ultrafast-response mechanism of the sensors was investigated using complex impedance spectroscopy and a quartz crystal microbalance (QCM).
In this paper, we present the development of a polymer optical fiber (POF) sensor for simultaneous measurement of breath (BR) and heart rates (HR). The sensor is embedded as a smart textile solution that can be used within the user’s clothes. In addition, a signal processing technique is proposed for obtaining the HR and BR without the influence of body movements and in different positions of the user’s chest. Sensor signal processing and analysis are made in the frequency domain and different filters are applied. Results show errors below 4 beats per minute and 2 breaths per minute for the HR and BR, respectively, even when the user is performing periodic body movements such as the ones induced by the gait. Thus, the proposed POF-based smart textile is a low-cost solution with good accuracy that can be readily applied in the remote monitoring of patients at home without disturbing their daily activities.
Synthetic piezoelectric polymer films produced from petroleum feedstock
have long been used as thin-film sensors and actuators. However, the
fossil fuel requirements for synthetic polymer production and carbon
dioxide emission from its combustion have raised concern about the
environmental impact of its continued use. Eco-friendly biomass
polymers, such as poly(L-lactic acid) (PLLA), are made from plant-based
(vegetable starch) plastics and, thus, have a much smaller carbon
footprint. Additionally, PLLA does not exhibit pyroelectricity or
unnecessary poling. This suggests the usefulness of PLLA films for the
human--machine interface (HMI). As an example of a new HMI, we have
produced a TV remote control using a PLLA film. The intuitive operation
provided by this PLLA device suggests that it is useful for the elderly
or handicapped.
Human heartbeat and respiration signals were measured from a DAQ board interfaced with a homemade voltage-amplifier with specific frequency filtering function using an elastic textile belt imbedded with a poled-PVDF film, which is designated as a physiological sensing belt (PSB), as a piezoelectric sensor and analyzed. PSB sensor systems can be easily tied on the chest, abdomen, head, wrist, or ankle for comfortable fit. The sensitivity of the imbedded PVDF film sensor is highly dependent on the thickness of coated silicon rubber layer on both sides of the PVDF film. Both respiration and heartbeat signals could be isolated from the mixture signal measured from the chest and abdomen during respiration and pulse wave velocity could be calculated using time delay obtained from waveforms measured simultaneously at the two different body positions. Finally we found the feasibility of applying the PSB as a complementary respiration monitoring system to the traditional anesthesia activities or other types of patient respiratory monitoring such as polysomnography and telemedicine.
Heart rate variability in the frequency domain has been proposed to reflect cardiac autonomic control. Therefore, measurement of heart rate variability may be useful to assess the effect of epidural anesthesia on cardiac autonomic tone. Accordingly, the effects of preganglionic cardiac sympathetic blockade by segmental epidural anesthesia were evaluated in humans on spectral power of heart rate variability. Specifically, the hypothesis that cardiac sympathetic blockade attenuates low-frequency spectral power, assumed to reflect cardiac sympathetic modulation, was tested.
Ten subjects were studied while supine and during a 15-min 40 degrees head-up tilt both before and after cardiac sympathetic blockade by segmental thoracic epidural anesthesia (sensory block: C6-T6). ECG, arterial pressure, and respiratory excursion (Whitney gauge) were recorded, and a fast-Fourier-transformation was applied to 512-s data segments of heart rate derived from the digitized ECG at the end of each intervention.
With cardiac sympathetic blockade alone and the subjects supine, both low-frequency (LF, 0.06-0.15 Hz) and high-frequency (HF, 0.15-0.80 Hz) spectral power remained unchanged. During tilt, epidural anesthesia attenuated the evoked increase in heart rate (+11.min-1 +/- 7 SD vs. +6 +/- 7, P = 0.024). However, while during tilt cardiac sympathetic blockade significantly decreased the LF/HF ratio (3.68 +/- 2.52 vs. 2.83 +/- 2.15, P = 0.041 vs. tilt before sympathetic blockade), a presumed marker of sympathovagal interaction, absolute and fractional LF and HF power did not change.
Although preganglionic cardiac sympathetic blockade reduced the LF/HF ratio during tilt, it did not alter spectral power in the LF band during rest or tilt. Accordingly, low-frequency spectral power is unlikely to specifically reflect cardiac sympathetic modulation in humans.
Respiratory sinus arrhythmia (RSA) is heart rate variability in synchrony with respiration, by which the R-R interval on an ECG is shortened during inspiration and prolonged during expiration. Although RSA has been used as an index of cardiac vagal function, it is also a physiologic phenomenon reflecting respiratory-circulatory interactions universally observed among vertebrates. Previous studies have shown that the efficiency of pulmonary gas exchange is improved by RSA, suggesting that RSA may play an active physiologic role. The matched timing of alveolar ventilation and its perfusion with RSA within each respiratory cycle could save energy expenditure by suppressing unnecessary heartbeats during expiration and ineffective ventilation during the ebb of perfusion. Furthermore, evidence has accumulated of a possible dissociation between RSA and vagal control of that heart rate, suggesting differential controls between the respiratory modulation of cardiac vagal outflow and cardiac vagal tone. RSA or heart rate variability in synchrony with respiration is a biological phenomenon, which may have a positive influence on gas exchange at the level of the lung via efficient ventilation/perfusion matching.
Beat-to-beat changes in cardiac signals or heart rate variability (HRV) are controlled by the two branches of autonomic nervous system (ANS) in a very complex manner. Although traditional HRV (tHRV) analysis has shown to provide information on cardiac ANS control, it often fails to isolate the effect of two branches in HRV signals. This problem becomes more obvious especially at low respiratory rates since parasympathetic activity shifts into lower frequencies and overlaps the frequency interval where sympathetic region is defined. To investigate the effect of respiration in HRV analysis we have analyzed the data of 17 healthy subjects while they were performing normal breathing (NB) and deep breathing (DB). Data were analyzed and compared using both tHRV analysis and enhanced HRV (eHRV) analysis where we used respiration to locate the frequency interval of parasympathetic activity in HRV signal. eHRV analysis provided proper isolation and more accurate detection of parasympathetic and sympathetic control of the heart.
We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.