Design of tree structured matched wavelet for HRV signals of menstrual cycle

ObjectiveCardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.MethodologyFor the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.ResultsNumerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

Aiming at problems such as the difficulty of recognizing emotions in the elderly and the inability of traditional machine-learning models to effectively capture the nonlinear relationship between physiological signal data, a Recursive Map (RM) combined with a Vision Transformer (ViT) is proposed to recognize the emotions of the elderly based on Electroencephalogram (EEG), Electrodermal Activity (EDA), and Heart Rate Variability (HRV) signals. The Dung Beetle Optimizer (DBO) is used to optimize the variational modal decomposition of EEG, EDA, and HRV signals. The optimized decomposed time series signals are converted into two-dimensional images using RM, and then the converted image signals are applied to the ViT for the study of emotion recognition of the elderly. The pre-trained weights of ViT on the ImageNet-22k dataset are loaded into the model and retrained with the two-dimensional image data. The model is validated and compared using the test set. The research results show that the recognition accuracy of the proposed method on EEG, EDA, and HRV signals is 99.35%, 86.96%, and 97.20%, respectively. This indicates that EEG signals can better reflect the emotional problems of the elderly, followed by HRV signals, while EDA signals have poorer effects. Compared with Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN), the recognition accuracy of the proposed method is increased by at least 9.4%, 11.13%, and 12.61%, respectively. Compared with ResNet34, EfficientNet-B0, and VGG16, it is increased by at least 1.14%, 0.54%, and 3.34%, respectively. This proves the superiority of the proposed method in emotion recognition for the elderly.

Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator’s mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject’s HRV signal to train the model, the subject’s mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators’ well-being and safety in the human-robot interaction process.

In this work, traditional, clinical methods of evaluating autonomic neural control systems are compared with the newer method based on the signal processing applied on heart rate variability (HRV) signal. The information available from the time-frequency representation of HRV signals is useful in classifying the subject population into 4 classes namely normal, sympathetic loss, parasympathetic loss and combined losses. The results suggest that the presence of autonomic neuropathy can be clearly detected by short time HRV signal analysis possible in the clinical environment.

Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index

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.

A non-invasive, clinical system for low-cost screening of diabetes mellitus (DM) and coronary heart disease (CHD) is introduced and tested on patients with known conditions. In particular, the heart rate variability (HRV) signal is extracted from 30 minutes of lead II electrocardiogram (ECG), during which the patient was requested to perform specific actions. The HRV signal is then characterized using time and frequency domain features which are subsequently used as a basis for classification into normal (N), DM, CHD, and diabetic CHD (CHDD) categories.

Diabetic Cardiac Autonomic Neuropathy (DCAN) has been found to be a precursor for Sudden Cardiac Death and several other serious heart diseases. The changes which occur in the frequency domain parameters of Heart Rate Variability (HRV) signal, extracted during the normal, relaxed state and during the deep breathing state are found to be excellent indicators of the neuropathic conditions of the heart. In the present work, an expert system has been developed to classify the type of neuropathy in each subject, based on the values of the frequency domain parameters calculated using the signal processing algorithms on the HRV signal. In this rule based expert system, six different classes are identified, namely normal, parasympathetic loss, sympathetic loss, mixed loss, neural imbalance and late parasympathetic action.

In this work, in order to determination of effect of a puzzle video game on HRV (Heart rate variability) signal, Poincare plot analysis is used. The linear and nonlinear dynamics of HRV were evaluated using HRV recordings obtained from each player at the time of play and rest. For this purpose, we have correlated the Poincaré plot descriptors with standard HRV measures derived from time- and frequency-domain methods. In addition, the correlations between the descriptors of the Poincaré plot along with the frequency and time domain measures of HRV have evaluated for the game stage. Statistically significant values were observed in eSport players in gaming and during resting. The obtained results show that play and rest can be separated by using the features obtained from the Poincare plot.

An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals

Presents results obtained applying a nonlinear noise reduction method to time series measured from heart rate variability (HRV) signals. Signals generated by nonlinear systems show broad-band spectra which makes it not useful to employ linear filters to obtain noise reduction without introducing signal distortion. The noise reduction method the authors used works directly in the reconstructed system space state. The method is applied to the HRV signals of 9 normal and 6 heart transplanted subjects providing more reliable estimation of D2, Hurst coefficients and /spl alpha/ slope of 1/f spectrum. Moreover the noise reduction leads to a better identification of HRV signal complex trajectories that can be clearly represented by delay maps.

Digital spectral analysis of Heart Rate Variability (HRV) signals provides quantitative markers of the Autonomic Nervous System (ANS) activity, underpinning a variety of physiologic processes. In this investigation, a robust algorithm was developed to derive HRV signals, their FFT- or AR-based spectra and their standard spectral features from either electrocardiogram (ECG) or photoplethysmographic (PPG) signals. ECG/PPG signals were first interpolated to a common sampling rate and detrended using a first order DC-notch IIR high-pass filter to remove very low frequencies. Next, the undecimated Discrete Wavelet Transform (DWT) Daubechies-6 family of filters was used to selectively remove some of the high-frequency subbands from the signals. The filtered ECG/PPG signals were then squared to increase the dynamic range of the target dominant peaks from which accurate peak-to-peak intervals could be extracted. A smart peak-tracking and monitoring algorithm was used to detect the peaks while enforcing a valid trend of the instantaneous heart rate. Once the peak-to-peak intervals were obtained, the HRV signal was determined using cubic spline interpolation to create a signal at a very low sampling rate of 1-4 Hz. Standard Power Spectrum Estimation (PSD) of the HRV signal was then performed to generate normalized LF, HF and LF/HF spectral features to quantify parasympathetic influences and sympathovagal balance.

In this article, the effect of stress on heart rate variability (HRV) is analysed, while a human mind is subjected to different stressful situations. The subjects had to go through a series of challenges that exaggerate their mental stress. The subjects had been given specific tasks which are both time bound and without any limitation of time. The HRV signals are taken before and after the completion of tasks. The quantification is done by fitting an ellipse on the constructed a 2D phase space of the HRV signal. But the 2D phase space plot is not able to distinguish HRV signals on normal and stressful conditions for all the time. In this paper, we introduce a 3D phase space plot in a spherical coordinate system which can distinguish normal and stressed conditions significantly. It has been observed that the volume of the cluster in reconstructed space increased drastically on stressed condition. The volume of the fitted ellipsoid on the reconstructed space is a proper quantification tool which can clearly distinguish two aforesaid conditions. Our experimental result provides a satisfactory result in this context.

Paroxysmal atrial fibrillation (PAF) is a temporary arrhythmic condition which is often a precursor of permanent/chronic atrial fibrillation. As frequent PAF events may easily lead to serious heart conditions, such as stroke, arterial embolism, it is propitious to have an early warning system. To this end, we propose an automated system for early prediction of PAF events based on statistical and nonlinear features extracted from heart rate variability (HRV) signal. We compute multiscale symbolic entropy, visibility graph-based complexity measures and three time-domain measures from the HRV signal. Out of them, the independent discriminative features are selected by Wilcoxon signed-rank test and correlation assessment. Finally, the selected features are applied to support vector machine (SVM), naive Bayes, and logistic regression classifiers to obtain the best prediction model. We achieve the best prediction results using radial basis function based SVM classifier with sensitivity, specificity and accuracy of 96.15%, 97.06%, 96.80% respectively, from the segments 5-10 mins before the onset of PAF events.

Vegetative Background of Sleep: Spectral Analysis of the Heart Rate Variability