Heart Rate Variability Signal Research Articles

Expert system for detection of congestive heart failure using optimal wavelet and heart rate variability signals for wireless cloud‐based environment

AbstractCongestive heart failure (CHF) is a cardiac disorder caused due to inefficient pumping of the heart, which leads to insufficient blood flow to the various parts of the body. The electrocardiogram (ECG) is widely used for the detection of heart diseases. However, it is prone to noise resulting in the detection of P, Q, R, S, and T waves ambiguous and erroneous. The heart rate variability (HRV) is considered to be a good indicator of various cardiac abnormalities. Hence, HRV is preferred. HRV can depict the magnitude of pumping of the heart in the RR interval signals accurately. This work proposes a method to automatically identify CHF using two‐band stopband energy (SBE) optimized orthogonal wavelet filter bank with HRV signals. In the proposed method, we have segmented the HRV data into lengths of 500 and 2000 samples. The HRV signals are decomposed into six sub‐bands, and the wavelet coefficients obtained are used for the extraction of fuzzy entropy (FE) and log energy (LE) features. The extracted features are utilized to classify HRV signals into control and CHF‐affected patients using support vector machine (SVM), bagged tree, complex tree, k‐nearest neighbour (KNN), and linear discriminant classifiers. The SVM performed better than other classifiers yielding the classification accuracy and maximum classification accuracy of 99.30% with (2000 samples) using cubic SVM (CSVM). The 10‐fold cross‐validation method is employed during classification to reduce the over‐fitting phenomenon (Sharma, Dhiman, & Acharya, 2021). It appears that the proposed optimal wavelet‐based automated system can identify CHF accurately using HRV signals. Hence, the model may be applied in clinical usage during an emergency employing a cloud‐based wireless system after testing the developed model with more data.

Relationship Between Heart Rate Variability and Pulse Rate Variability Measures in Patients After Coronary Artery Bypass Graft Surgery.

Background: Heart rate variability (HRV) and pulse rate variability (PRV) measures are two kinds of physiological indices that can be used to evaluate the autonomic nervous function of healthy subjects and patients with various kinds of illness.Purpose: In this study, we compared the agreement and linear relationship between electrocardiographic signals (ECG)-derived HRV and photoplethysmographic signals (PPG)-derived right hand PRV (R-PRV) and left hand PRV (L-PRV) measures in 14 patients over 1 year after coronary artery bypass graft (CABG) surgery.Method: The ECG and PPG signals of the patient were recorded simultaneously for 10 min in a supine position. The last 512 stationary RR intervals (RRI) and peak-to peak intervals (PPI) of pulse wave were derived for data analysis. Bland-Altman plot was used to assess the agreement among HRV and both hand PRV measures, while linear regression analysis was used to examine the relationship among corresponding measures of HRV, R-PRV, and L-PRV.Result: The results revealed significant differences in total power (TP), very low-frequency power (VLF), low-frequency power (LF), high-frequency power (HF), and normalized VLF (VLFnorm) among HRV, R-PRV, and L-PRV. Bland-Altman plot analysis showed good agreements in almost all measures between R-PRV and L-PRV, except insufficient agreement was found in LF/HF. Insufficient agreements were found in root mean square successive difference (RMSSD), normalized HF (HFnorm), and LF/HF indices between HRV and L-PRV, and in VLFnorm, HFnorm, and LF/HF indices between HRV and R-PRV. Linear regression analysis showed that the HRV, R-PRV, and L-PRV measures were all highly correlated with one another (r = 0.94 ~ 1; p < 0.001).Conclusion: Though PRV measures of either hand are not surrogates of HRV measures, they might still be used to evaluate the autonomic nervous functions of CABG patients due to the moderate to good agreements in most time-domain and frequency-domain HRV measures and the strong and positive correlations among HRV and both hands PRV measures in CABG patients.

The Influence of an Alpha Band Neurofeedback Training in Heart Rate Variability in Athletes.

Neurofeedback training is a technique which has seen a widespread use in clinical applications, but has only given its first steps in the sport environment. Therefore, there is still little information about the effects that this technique might have on parameters, which are relevant for athletes’ health and performance, such as heart rate variability, which has been linked to physiological recovery. In the sport domain, no studies have tried to understand the effects of neurofeedback training on heart rate variability, even though some studies have compared the effects of doing neurofeedback or heart rate biofeedback training on performance. The main goal of the present study was to understand if alpha-band neurofeedback training could lead to increases in heart rate variability. 30 male student-athletes, divided into two groups, (21.2 ± 2.62 year 2/week protocol and 22.6 ± 1.1 year 3/week protocol) participated in the study, of which three subjects were excluded. Both groups performed a pre-test, a trial session and 12 neurofeedback sessions, which consisted of 25 trials of 60 s of a neurofeedback task, with 5 s rest in-between trials. The total neurofeedback session time for each subject was 300 min in both groups. Throughout the experiment, electroencephalography and heart rate variability signals were recorded. Only the three sessions/week group revealed significant improvements in mean heart rate variability at the end of the 12 neurofeedback sessions (p = 0.05); however, significant interaction was not found when compared with both groups. It is possible to conclude that neurofeedback training of individual alpha band may induce changes in heart rate variability in physically active athletes.

Уменьшение спектральной плотности интервалов сердечных сокращений в низкочастотном диапазоне у пациентов с артериальной гипертензией в математической модели кровообращения

Уменьшение спектральной плотности интервалов сердечных сокращений в низкочастотном диапазоне у пациентов с артериальной гипертензией в математической модели кровообращения

Autonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability

Objectives: an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed. Methods: ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response. Results: PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive. Conclusions: PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time-domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In sum, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is needed for better interpretation of the results.

Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy

Background. Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals. Objective. To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case. Approach. Simulations were used to evaluate the model order selection when calculating the RSA estimates introduced before, as well as three different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals. Main results. It was found that using a single model order for all the calculations suffices to characterize RSA correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 min might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration. Significance. Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.

The Added Value of Nonlinear Cardiorespiratory Coupling Indices in the Assessment of Depression.

The present study investigates the differences in autonomic nervous system (ANS) function and stress response between patients with major depressive disorder (MDD) and healthy subjects by measuring changes in ANS biomarkers. ANS-related parameters are derived from various biosignals during a mental stress protocol consisting of a basal, stress, and recovery phase. The feature set consists of ANS biomarkers such as the heart rate (HR) derived from the electrocardiogram, the respiratory rate derived from the respiration signal, vascular parameters obtained from a model-based photoplethysmographic pulse waveform analysis, and cardiorespiratory coupling indices derived from the joint analysis of the heart rate variability (HRV) and respiratory signals. In particular, linear cardiorespiratory interactions are quantified by means of time-frequency coherence, while interactions of quadratic nonlinear nature between HRV and respiration are quantified by means of real wavelet biphase. The intra-subject difference of a feature value between two phases of the protocol, the so-called autonomic reactivity, is considered as a ANS biomarker as well. The performance of ANS biomarkers on discriminating MDD patients is evaluated using a classification pipeline. The results show that the most discriminative ANS biomarkers are related with differences in HR and autonomic reactivity of both vascular and nonlinear cardiorespiratory coupling indices. Differences in autonomic reactivity imply that MDD and healthy subjects differ in their ability to cope with stress. Considering only HR and vascular characteristics a linear support-vector machine classifier yields to accuracy 72.5% and F1-score 73.2%. However, taking into account the nonlinear cardiorespiratory coupling indices, the classification performance improves, yielding to accuracy 77.5% and F1-score 78.0%.Clinical relevance- Changes in the nonlinear properties of the cardiorespiratory system during stress may yield additional information on the assessment of depression.

Analysis of Heart Rate Variability in Normal and Diabetic ECG signals using Fragmentation Approach.

In this work, an attempt is made to quantify the dynamics of the heart rate variability timeseries in normal and diabetic population using fragmentation metrics. ECG signals recorded during deep breathing and head tilt up experiments are utilized for this study. The QRS-wave of ECG is extracted using the Pan Tompkins Algorithm. Heart rate variability features such as heart rate, Percentage of Inflection Points (PIP) and Inverse of the Average Length of the acceleration/deceleration Segment (IALS) are extracted to quantify the variation in signal dynamics. The results indicate that the ECG signals and heart rate variability signals obtained in deep breathing and tilt exhibit varied characteristics in both normal and diabetics. Further, in the diabetic condition the fragmentation measures exhibit a higher value in both deep breathing and tilt which indicates increased alternations in the signal. Most of the extracted fragmentation features are statistically significant (p<0.005) in differentiating normal and diabetic population. It appears that this method of analysis has potential towards the development of systems for the noninvasive assessment of diabetes.Clinical Relevance- This establishes a technique to quantify the variation in cardiovascular dynamics in normal and diabetic population.

Review of Deep Learning-Based Atrial Fibrillation Detection Studies.

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.

Psychophysiological effects of slow-paced breathing at six cycles per minute with or without heart rate variability biofeedback.

Heart rate variability (HRV) biofeedback, referring to slow-paced breathing (SPB) realized while visualizing a heart rate, HRV, and/or respiratory signal, has become an adjunct treatment for a large range of psychologic and medical conditions. However, the underlying mechanisms explaining the effectiveness of HRV biofeedback still need to be uncovered. This study aimed to disentangle the specific effects of HRV biofeedback from the effects of SPB realized alone. In total, 112 participants took part in the study. The parameters assessed were emotional (valence, arousal, and control) and perceived stress intensity as self-report variables and the root mean square of the successive differences (RMSSD) as a physiologic variable. A main effect of condition was found for emotional valence only, valence being more positive overall in the SPB-HRVB condition. A main effect of time was observed for all dependent variables. However, no main effects for the condition or time x condition interaction effects were observed. Results showed that for PRE and POST comparisons (referring, respectively, to before and after SPB), both SPB-HRVB and SPB-NoHRVB conditions resulted in a more negative emotional valence, lower emotional arousal, higher emotional control, and higher RMSSD. Future research might investigate psychophysiological differences between SPB-HRVB and SPB-NoHRVB across different time periods (e.g., long-term interventions), and in response to diverse psychophysiological stressors.

Prediction of atrial fibrillation based on nonlinear modeling of heart rate variability signal and SVM classifier

Prediction of atrial fibrillation based on nonlinear modeling of heart rate variability signal and SVM classifier

Development of a biofeedback system using harmonic musical intervals to control heart rate variability with a generative adversarial network

Biofeedback methods supply biological information to subjects in real-time. Target biomedical/physiological variables are measured and fed back either directly, or transformed beforehand. Biofeedback based on Heart Rate Variability (HRV) has shown to have positive effects in managing anger, sleep, general quality of life and the restoration of physiological homeostasis. The literature suggests the use of new technologies to deal with interpreting the information and determining how Generative Adversarial Networks (GANs) might help in transforming information that flows through a biofeedback system. Given the wide use of Musical Instrument Digital Interface (MIDI) and piano-rolls, and the importance of HRV in the fields of health and perception, a GAN-based biofeedback system was proposed to generate MIDI sequences of harmonic musical intervals (HMI) from HRV signals. The proposed system seeks to produce, by changing HMI sounds, desirable respective HRV responses. The system was tested simulating HRV responses to HMI stimuli (the global health threat excluded real-life testing). Simulation was performed using a one-dimensional GAN. Rather than from latent space, one GAN generator was fed from raw HRV and the other from sound signals. An association was distinguished between the captured HRV signals and the stimuli, since GAN can generate representations similar to the stimuli employed. As a biofeedback system, the subjects provide HRV to feed the GANs and can be stimulated with sounds produced by an HMI generator. This system opens doors in the fields of perception and stress management, and might be applied in neuromarketing and human–computer interfaces.

Characterization and classification of Parkinson’s disease patients based on symbolic dynamics analysis of heart rate variability

Background: Parkinson’s disease (PD) is a chronic and progressive neurodegenerative disorder characterized by deterioration of the substantia nigra, resulting in a deficiency of dopamine. PD is considered a movement disorder associated with numerous non-motor symptoms related to Autonomic Nervous System failures which can precede the motor ones. Therefore, their awareness could be helpful in the diagnosis of PD at an early stage.Methods: Heart Rate Variability (HRV) is assessed by time and frequency domain indices, and by nonlinear indices based on symbolic dynamics and multiscale symbolic entropy. The features obtained were used to classify between PD patients and control volunteers using a support vector machine. Volunteers performed cardiovascular autonomic reflex tests: active standing, post- hyperventilation and controlled breathing.Results: Temporal and frequency indices showed significantly lower values in PD patients compared to control volunteers. Symbolic dynamics and multiscale symbolic entropy results suggest a decrease in the complexity of the HRV signal in PD patients, in contrast with a more variable pattern of words for control volunteers. During controlled breathing differences between groups were found with most of the indices computed. Additionally, classification process achieves good separability during cardiorespiratory maneuvers (>95% of accuracy) and features based on symbolic dynamics showed high discrimination between groups.Conclusions: The results found in this work suggest that the proposed methodological approach can classify PD patients in an early disease stage from healthy controls and give additional information about the cardiorespiratory system, which could be useful for diagnosis and follow up of PD patients.

Machine-Deep-Ensemble Learning Model for Classifying Cybersickness Caused by Virtual Reality Immersion.

This study aims to classify cybersickness (CS) caused by virtual reality (VR) immersion through a machine-deep-ensemble learning model. The heart rate variability and respiratory signal parameters of 20 subjects were measured, while watching a VR video for ∼5 minutes. After the experiment, the subjects were examined for CS and questioned to determine their CS states. Based on the results, we constructed a machine-deep-ensemble learning model that could identify and classify VR immersion CS among subjects. The ensemble model comprised four stacked machine learning models (support vector machine [SVM], k-nearest neighbor [KNN], random forest, and AdaBoost), which were used to derive prediction data, and then, classified the prediction data using a convolution neural network. This model was a multiclass classification model, allowing us to classify subjects' CS into three states (neutral, non-CS, and CS). The accuracy of SVM, KNN, random forest, and AdaBoost was 94.23 percent, 92.44 percent, 93.20 percent, and 90.33 percent, respectively, and the ensemble model could classify the three states with an accuracy of 96.48 percent. This implied that the ensemble model has a higher classification performance than when each model is used individually. Our results confirm that CS caused by VR immersion can be detected as physiological signal data with high accuracy. Moreover, our proposed model can determine the presence or absence of CS as well as the neutral state. Clinical Trial Registration Number: 20-2021-1.

Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods

Objective. The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls. Approach. The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method. Main results. The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, −17.5%; univariate AR, −18.3%; bivariate AR, −23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing. Significance. Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.

Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study.

BackgroundPrevious research has highlighted the role of stress in substance misuse and addiction, particularly for relapse risk. Mobile health interventions that incorporate real-time monitoring of physiological markers of stress offer promise for delivering tailored interventions to individuals during high-risk states of heightened stress to prevent alcohol relapse. Before such interventions can be developed, measurements of these processes in ambulatory, real-world settings are needed.ObjectiveThis research is a proof-of-concept study to establish the feasibility of using a wearable sensor device to continuously monitor stress in an ambulatory setting. Toward that end, we first aimed to examine the quality of 2 continuously monitored physiological signals—electrodermal activity (EDA) and heart rate variability (HRV)—and show that the data follow standard quality measures according to the literature. Next, we examined the associations between the statistical features extracted from the EDA and HRV signals and self-reported outcomes.MethodsParticipants (N=11; female: n=10) were asked to wear an Empatica E4 wearable sensor for continuous unobtrusive physiological signal collection for up to 14 days. During the same time frame, participants responded to a daily diary study using ecological momentary assessment of self-reported stress, emotions, alcohol-related cravings, pain, and discomfort via a web-based survey, which was conducted 4 times daily. Participants also participated in structured interviews throughout the study to assess daily alcohol use and to validate self-reported and physiological stress markers. In the analysis, we first used existing artifact detection methods and physiological signal processing approaches to assess the quality of the physiological data. Next, we examined the descriptive statistics for self-reported outcomes. Finally, we investigated the associations between the features of physiological signals and self-reported outcomes.ResultsWe determined that 87.86% (1,032,265/1,174,898) of the EDA signals were clean. A comparison of the frequency of skin conductance responses per minute with previous research confirmed that the physiological signals collected in the ambulatory setting were successful. The results also indicated that the statistical features of the EDA and HRV measures were significantly correlated with the self-reported outcomes, including the number of stressful events marked on the sensor device, positive and negative emotions, and experienced pain and discomfort.ConclusionsThe results demonstrated that the physiological data collected via an Empatica E4 wearable sensor device were consistent with previous literature in terms of the quality of the data and that features of these physiological signals were significantly associated with several self-reported outcomes among a sample of adults diagnosed with alcohol use disorder. These results suggest that ambulatory assessment of stress is feasible and can be used to develop tailored mobile health interventions to enhance sustained recovery from alcohol use disorder.

HREmo: A measurement system of students’ studying state in online group class based on rPPG technology

The CONVID-19 Pandemic has broken the traditional teaching method, but ignorance of students’ feedback made many teachers feel the teaching quality was degraded. Although some researchers have proposed that whether students are attentive or dozing off by analysing student expressions, head corners, and eye postures, But it is difficult to adapt to the situation where students frequently lower their heads to take notes in actual situation. Therefore, this paper proposes a heart rate acquisition and analysis system HREmo based on remote photoplethysmography technology(rPPG). The time and frequency domain characteristics of the heart rate variability signal are further analysed through the heartbeat interval obtained by the pulse waveform. It has multiple advantages such as bases on objective indicators, calculates in fast speed and low cost. The recognition error of heart rate is within 1%, and it has accurate trend prediction for students’ concentration and mental workload. Furthermore, this article takes a group class as an example to describe in detail how to put HREmo in the teacher panel to help teachers provide students with more personalized status reminders.

Time Domain Analysis of Heart Rate Variability Signals in Valence Recognition for Children with Autism Spectrum Disorder (ASD)

BackgroundAutism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by various social impairments. Children with ASD have major difficulties in expressing themselves, resulting in stress and meltdowns. Understanding their hidden feelings and needs may help in tackling and avoiding such strenuous behaviors. ObjectiveThis research aims to aid the parents and caretakers of children with ASD to understand the hidden and unexpressed emotional state by using physiological signals obtained from wearable devices. MethodsHere, electrocardiogram (ECG) signals pertaining to two valence states (‘like’ and ‘dislike’) were recorded from twenty children (10 Control and 10 children with ASD). The heart rate variability (HRV) signals were then obtained from the ECG signals using the Pan-Tompkins's algorithm. The statistical, higher order statistics (HOS) and geometrical features which were statistically significant were trained using the K Nearest Neighbor (KNN) and Ensemble Classifier algorithms. ResultsThe findings of our analysis indicate that the integration of major statistical features resulted in an overall average accuracy of 84.8% and 75.3% using HRV data for the control and test population, respectively. Similarly, geometrical features resulted in a maximum average accuracy of 84.8% and 74.2% for control and test population respectively. The decreased HRV in the test population indicates the presence of autonomic dysregulation in children with ASD when compared to their control peers.

Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia.

In recent decades, little progress of objective evaluation of pain and noxious stimulation has been achieved under anesthesia. Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. In the process of data processing, the empirical mode decomposition is used to decompose and recombine heart rate variability signals, and the sliding time window approach is used to extract the signal features of noxious stimulation, respectively. The influence of window size on feature extraction is studied by changing the window size. By comparing the results, the feature extraction in the process of data processing is valuable, and the selection of window size has a significant impact. With the increase of selected window sizes, we can get better detection results. But for the best choice of window size, to ensure the accuracy of the results and to make it easy to use, then, we need to get just a suitable window size.

Automated Detection of Hypertension Using Physiological Signals: A Review.

Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.