Hidden Markov Models for Heart Rate Variability with Biometric Applications

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.

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.

Heart rate variability and cognitive function: Effects of physical effort

An algorithm is presented for designing a new class of wavelets matched to the Heart Rate Variability (HRV) signals of the menstrual cycle. The proposed wavelets are used to find HRV variations between phases of menstrual cycle. The method finds the signal matching characteristics by minimising the shape feature error using Least Mean Square method. The proposed filter banks are used for the decomposition of the HRV signal. For reconstructing the original signal, the tree structure method is used. In this approach, decomposed sub-bands are selected based upon their energy in each sub-band. Thus, instead of using all sub-bands for reconstruction, sub-bands having high energy content are used for the reconstruction of signal. Thus, a lower number of sub-bands are required for reconstruction of the original signal which shows the effectiveness of newly created filter coefficients. Results show that proposed wavelets are able to differentiate HRV variations between phases of the menstrual cycle accurately than standard wavelets.

Commonly, parents decide to have children to complete their live as part of their family. Then, how about the parents who have another reason with the presence of their children? It can make the children to have different thinking with other and it can make the children wonder about their true status or identity in family. It is because parents and their children do not have the similar purpose in their family. This situation happened in My Sister’s Keeper novel. The researcher choose to analyse Anna Fitzgerald in the novel because the life of family in My Sister’s Keeper novel was not like other family in general. The researcher analysed Anna Fitzgerald’s character through two theories to find out her identity. Erikson’s psychosocial development in the fifth stage was used to analyse of how Anna Fitzgerald got through the development as thirteen years old children in defining her own identity, successfully or not. It was because this stage discusses about the process of finding the true identity. Meanwhile, the researcher used Bowlby’s attachment theory to analyse of Anna’s relationship with her mother. Also, in this theory, the researcher only used one characteristic (proximity maintenance) to know about factor that influence of Anna’s position in Erikson’s fifth stage. This research was aimed to find out about the identity in fifth stage of psychosocial development of a child who lived uncommon situation and the factor that influenced her identity. The result of this research showed that Anna psychosocially experienced role confusion because she failed to know about her true identity. This situation influenced Anna’s relationship with her mother. As a result, she did not have fidelity as her basic virtue. Anna did not get the sense of safety and comfort from her relationship with Sara, as mother-daughter relationship . Key words: Psychosocial Development, Attachment Theory

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.

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.

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.

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.

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.

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

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.

Objectives: Continuous connected breathing (CCB) is a high-frequency breathing technique without breath retention. This study examines changes in electroencephalogram (EEG) band power and heart rate variability (HRV) before, during and after breathwork intervention of 3 rounds of 40 CCB versus normal breathing in healthy volunteers. Materials and Methods: Thirty-three healthy volunteers, aged between 19 and 23 years, participated in the study (n = 15; female = 7; mean age = 21.5 years) group, while the control group consisted of 18 individuals (n = 18; male = 11; mean age = 19.6 years). After acquiring baseline measures, the study group engaged in 40 CCB practice online for 10 days, while the control group continued with their usual activities. EEG and HRV signals were recorded for both groups after the 10 days. Post-intervention, the study group practised 40 CCB breathwork, while the control group breathed normally during signal acquisition. EEG was recorded for 1-min durations at five different time events (1st, 4th, 5th, 7th and 10th min) according to the protocol. HRV was continuously recorded for the entire 10 min, and 1-min readings corresponding to the EEG recording time intervals were analysed. EEG signals were acquired using an Enobio-8 cap with dry electrodes in pre-frontal (FP1 and FP2), temporal (T7 and T8), central (C3 and C4) and occipital (O1 and O2) regions. HRV data were acquired from Polar H7/H10 chest strap sensors through Bluetooth using Elite HRV app on android phone and data were later processed with Kubios software. EEG signals were processed using MATLAB software to extract EEG power for delta (0– 3.5Hz), theta (4–7 Hz), alpha (8–13 Hz), beta (14–29 Hz) and gamma frequencies (30–50 Hz). The EEG power and HRV signals were later arranged as before-after and during time events and statistically analysed for within and between-group changes using t-tests for significance of means. Results: There was an overall quantitative change in EEG band power of −2 units within the study group and −0.03 within the control group. A significant decrease was found in overall delta (−2.27), theta (−1.7), alpha (−1.8) and beta (−2.27) brain waves in pre-frontal (FP1, FP8), temporal (T7, T8) and occipital (O1, O2) regions after breathwork compared to before within the study group. A significant increase was found in beta (+2.8) band power in the control group with a higher increase in the left compared to the right hemisphere. Bilateral average change ranged from −1.6 to −2.6 in the study and −0.1 to +4.9 in the control group. HRV parameters were reduced after intervention in both groups but without significance. The parameters improved after the intervention compared to during within the study group, though not significant. Conclusion: 10 days of practice of 40 CCB breathworks (3 rounds) for 5 min/day may reduce EEG power in delta, theta, alpha and beta waves. It may help to maintain autonomic balance with an increase of HRV in the recovery phase after an initial decrease during breathwork. Further studies are recommended to test the consistency of outcomes.

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.

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