Effect of 40 continuous connected breathing on electroencephalogram and heart rate variability of healthy volunteers

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.

Apneas occurring during sleep may precipitate autonomic instability in epilepsy patients making them susceptible to sudden death (SUDEP). Literature on heart rate variability (HRV) during apnea among patients with temporal lobe epilepsy (TLE) is sparse. The aim of this study was to characterize the HRV during the peri-apneic period in patients with TLE and compare with HRV of matched healthy individuals during the overnight polysomnographic (PSG) recording. Further, the role of carbamazepine (CBZ) in modulating peri-apneic HRV in the above cohort was also assessed. Twenty patients diagnosed to have TLE (drug naive (n=10) or on CBZ monotherapy (n=10)) were compared with ten healthy controls. In both patients and controls, the time domain, frequency domain, and non-linear HRV indices were analyzed for 2min before and after apnea/hypopnea termination and compared using paired t test (p≤0.05). Additionally, the changes in HRV parameters in the peri-apnea/hypopnea period were compared between the three groups using one-way ANOVA followed by post hoc comparison (p≤0.05). The three study groups were age (p=0.21) and gender (p=0.27) matched. In controls (M/F=5:5; mean age 24.3±5.0years), there were significant changes in standard deviation of RR interval (SDNN), low frequency (LF) component and long-term HRV (SD2) parameters in the peri-apnea/hypopneic period. Conversely, in drug-naive TLE (M/F=6:4; mean age: 22.8±4.1years), all the HRV parameters, including non-linear measures were comparable in the pre- and post-apneic period. On the other hand, patients on CBZ (M/F=6:4; mean age 20.5±4.8years) showed significant changes in low-frequency nu (LFnu) and high-frequency nu (HFnu) components in the peri-apnea/hypopneic period. Comparison of the changes in HRV parameters in the peri-apnea/hypopnea period in patients with TLE and controls showed significantly lower changes in drug-naive TLE patients in SDNN, LF, and SD2 as compared to controls. This study showed that there was a lack of apnea-mediated HRV changes in patients with drug-naive TLE. This might suggest a possible alteration in reflex baroreceptor activation in patients with TLE, predisposing them to SUDEP, and this may be worsened with CBZ.

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.

Clinical utility of paced breathing as a concentration meditation practice

To compare intraocular pressure (IOP) control in eyes with or without clear corneal phacoemulsification following trabeculectomy. The study group included 30 eyes that underwent uneventful clear corneal phacoemulsification and foldable intraocular lens implantation following trabeculectomy without antimetabolites. Thirty eyes that had undergone filtering surgery without cataract extraction were selected as controls. Case and control groups were matched with respect to age, gender, IOP, number of glaucoma medications, glaucoma type (primary open-angle glaucoma/pseudoexfoliative glaucoma), trabeculectomy time and follow-up. Comparisons between the study and control groups (intergroup) and within the same group at different time-points (intragroup) were performed for IOP, glaucoma medications and bleb morphology. Success rates were investigated by Kaplan-Meier survival analysis and the factors influencing final success by logistic regression. Intraocular pressure (p = 0.04) and glaucoma medications (p = 0.001) increased during an average follow-up of 26.1 +/- 9.9 months in both groups. Intragroup differences became statistically significant after the 6-month visit, but intergroup differences remained insignificant. Bleb height decreased significantly following phacoemulsification in the study group (p = 0.017). Success rates decreased with time in both groups, with no intergroup difference (p = 0.46). The final success rate was negatively correlated with IOP and number of glaucoma medications used at the study entry, while there was a positive correlation between the baseline and final success rates. Trabeculectomy success decreased in a time-dependent manner in eyes with and without subsequent phacoemulsification. Uncomplicated clear corneal phacoemulsification was not found to have any additional unfavorable influence on IOP control in eyes with filtering blebs.

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.

Background:Acute ischemic stroke (AIS) induces adverse effects on the cardiovascular system by affecting the autonomic nervous system (ANS).Objectives:This study aimed to determine whether the parameters of heart rate variability (HRV) and heart rate turbulence (HRT) differed in patients with AIS as compared to that in the control group. Furthermore, we aimed to determine the differences in the involvement of the ANS between right and left hemisphere (LH) strokes.Methods:A total of 148 [74 right hemispheres (RH) and 74 left hemispheres] patients with AIS and 80 control subjects were included in the study. The Holter device was used to obtain elcctrocardiogram readings for over 20 h from all patients. Results of HRV and HRT parameters [Tonset (TO) and Tslope (TS)] were acquired through an automatic analysis of the program.Results:All HRV parameters were found to be low in patients with AIS (P < 0.05, for all parameters). TO and TS were disrupted in 99 patients with AIS (66.8%) and in 15 control subjects (18.7%) (HRT–1 and HRT–2 groups, P = <0.001). HRV parameters were detected to be similar in patients, irrespective of the left or right infarct. TO and TS were normal in 31 patients (41.9%) with left hemisphere localization and in only 18 patients (24.3%) with right hemisphere localization.Conclusions:Combined evaluation of HRV and HRT parameters may provide important information regarding the alterations in the ANS in patients with AIS. The utility of HRT in the determination of ANS alterations in patients with AIS should be investigated in larger future prospective studies.

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 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.

Dissociation between ACE activity and autonomic response to ACE inhibition in patients with heart failure

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.

Eating Disorder (ED) patients who were admitted to an eating disorders program for a 6-week treatment and were assessed for changes in heart rate variability (HRV). Linear heart rate variability measures (HRV) were determined using Kubios software from 20 minute, Lead 3 ECG recordings following a 5-minute rest period. Eighteen patients and 31 controls were included in the analysis. Results were deemed significant if p<0.05. No significant differences in HRV parameters were noted for the control group between Pre- and Post-treatment. For the ED group, mean RR interval length decreased significantly compared to the control group (p=0.006). HF peak frequency increased significantly in the ED group (p=0.001). High frequency (HF) peak indicated that the respiratory frequency increased from Pre to Post measurement. RMSSD tended to decrease (p=0.047) in the ED group. Hence at admission to hospital the ED group was more parasympathetic during rest compared to controls, but they became more sympathetic after the intervention and thus approached the HRV measures of the controls suggesting improvement in ANS modulation of the heart.