Heart Rate Variability Features Research Articles

Towards Quantifying Stress in Patients with a History of Myocardial Infarction: Validating ECG-Derived Patch Features.

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance- This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning.

Heart failure refers to the inability of the heart to pump enough amount of blood to the body. Nearly 7 million people die every year because of its complications. Current gold-standard screening techniques through echocardiography do not incorporate information about the circadian rhythm of the heart and clinical information of patients. In this vein, we propose a novel approach to integrate 24-hour heart rate variability (HRV) features and patient profile information in a single multi-parameter and color-coded polar representation. The proposed approach was validated by training a deep learning model from 7,575 generated images to predict heart failure groups, i.e., preserved, mid-range, and reduced left ventricular ejection fraction. The developed model had overall accuracy, sensitivity, and specificity of 93%, 88%, and 95%, respectively. Moreover, it had a high area under the receiver operating characteristics curve (AUROC) of 0.88 and an area under the precision-recalled curve (AUPR) of 0.79. The novel approach proposed in this study suggests a new protocol for assessing cardiovascular diseases to act as a complementary tool to echocardiography as it provides insights on the circadian rhythm of the heart and can be potentially personalized according to patient clinical profile information.Clinical relevance- Implementing polar representations with deep learning in clinical settings to supplement echocardiography leverages continuous monitoring of the heart's circadian rhythm and personalized cardiovascular medicine while reducing the burden on medical practitioners.

Non-invasive Coronary Artery Disease Screening Based on Electrocardiogram Characteristics and Clinical Risk Factors.

Coronary artery disease (CAD), an acute and life-threatening cardiovascular disease, is a leading cause of mortality and morbidity worldwide. Coronary angiography, the principal diagnostic tool for CAD, is invasive, expensive, and requires a lot of skilled effort. The current study aims to develop an automated and non-invasive CAD detection model and improve its performance as closely as possible to clinically acceptable diagnostic sensitivity. Electrocardiogram (ECG) characteristics are observed to be altered due to CAD and can be studied to develop a screening tool for its detection. The subject's clinical information can help broadly identify the high-cardiac-risk population and serve as a primary step in diagnosing CAD. This paper presents an approach to automatically detect CAD based on clinical data, morphological ECG features, and heart rate variability (HRV) features extracted from short-duration Lead-II ECG recordings. A few popular machine-learning classifiers, including support vector machine (SVM), random forest (RF), K-nearest neighbours (KNN), Gaussian Naïve Bayes (GNB), and multi-layer perceptron (MLP), are trained on the extracted feature space, and their performance is evaluated. Classifiers built by integrating clinical data and features extracted from ECG recordings demonstrated better performance than those built on each feature set separately, and the RF classifier outperforms other considered machine learners and reports an average testing accuracy of 94% and a G-mean score of 92% with a 5-fold cross-validation training accuracy of 95(± 0.04)%.Clinical relevance- The proposed method uses a brief, single-lead ECG recording and performs similarly to current clinical practices in an explainable manner. This makes it suitable for deployment via wearable technology (like smart watch gadgets) and telemonitoring, which may facilitate an earlier and more widespread CAD diagnosis.

Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning.

Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. Epileptic siezure prediction systems face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model, which is able to achieve enhanced capability by utilizing a significant number of seizure patterns from globally distributed patients while maintaining data privacy. The determination of the preictal state is influenced by global and local model-assisted decision making by modeling the two-level edge layer. The Spiking Encoder (SE), integrated with the Graph Convolutional Neural Network (Spiking-GCNN), works as the local model trained using a bi-timescale approach. Each local model utilizes the aggregated seizure knowledge obtained from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. Thus, the proposed approach achieved 96.33% sensitivity and 96.14% specificity when tested on the CHB-MIT EEG dataset when modeling was performed using the bi-timescale approach and Spiking-GCNN-based epileptic pattern learning. Moreover, the adoption of federated learning greatly assists the proposed system, yielding a 96.28% higher accuracy as a result of addressing data scarcity.

An IOT framework for detecting cardiac arrhythmias in real-time using deep learning resnet model

A cardiac arrhythmia poses a serious health risk to patients and can have serious consequences for their health. A clinical assessment of arrhythmia disorders could save a person's life. The Internet of Things (IoT) will revolutionize the healthcare sector by continuously monitoring cardiac arrhythmia diseases remotely and minimally invasively. We propose a frame-work that will facilitate the development of a practical diagnostic tool for the identification of cardiac arrhythmias in real-time in this work. An Electrocardiogram (ECG) signal is processed using the Pan Tompkins QRS (Quantum Resonance System) detection method in order to extract the dynamic properties of the signal. The inter beat (RR) intervals are derived from an ECG signal in order to determine the characteristics of heart rate variability. The electrocardiogram is primarily used to identify irregular heartbeats (cardiac arrhythmias). Therefore, in our study, we evaluated other factors such as the heartbeat of the individual. As part of our IoT deployment, we are storing and analyzing data collected by the Pulse Sensor on the ThingSpeak IoT platform. The designed circuit's real-time collection of heartbeat and beats per minute values was uploaded to Thingspeak. Over the course of more than a week, we collected a variety of heart data. We propose Multi Channel Residual Network (MCHResNet) a deep-learning based solution that relies on multi-channel convolutions to detect both spatial and frequency features from electrocardiograms to facilitate the classification process. Based on the well-known Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia (MIT-BIH-AR) database, we evaluate the proposed framework against MCH ResNet. Our IoT-based framework has been shown to be effective based on the results reported in this paper.

Can heart rate variability data from the Apple Watch electrocardiogram quantify stress?

Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations (p < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device.

Identification and Classification of Physical Fatigue in Construction Workers Using Linear and Nonlinear Heart Rate Variability Measurements

Several studies have analyzed heart rate variability (HRV) using nonlinear methods, such as approximate entropy, the largest Lyapunov exponent, and correlation dimension in patients with cardiovascular disorders. However, few studies have used nonlinear methods to analyze HRV in order to determine the level of physical fatigue experienced by construction workers. As a result, to identify and categorize physical fatigue in construction workers, the current study examined the linear and nonlinear approaches of HRV analysis. Fifteen healthy construction workers (mean age, 33.2±6.9 years) were selected for this study. A textile-based wearable sensor monitored each participant’s HRV after they completed 60 min of bar bending and fixing tasks. At baseline, 15, 30, 45, and 60 min into the task, participants were given the Borg-20 to measure their subjective levels of physical fatigue. Nonlinear [e.g., R-R interval (RRI) variability, entropy, detrended fluctuation analysis] and linear (e.g., time- and frequency-domain) HRV parameters were extracted. Five machine learning classifiers were used to identify and discern different physical fatigue levels. The accuracy and validity of the classifier models were evaluated using 10-fold cross-validation. The classification models were developed by either combining or individualized HRV features derived from linear and nonlinear HRV analyses. In the individualized feature sets, time-domain features had the highest classification accuracy (92%) based on the random forest (RF) classifier. The combined feature (i.e., the time-domain and nonlinear features) sets showed the highest classification accuracy (93.5%) using the RF classifier. In conclusion, this study showed that both linear and nonlinear HRV analyses can be used to detect and classify physical fatigue in construction workers. This research offers important contributions to the industry by analyzing the variations in linear and nonlinear HRV parameters in response to construction tasks. This study demonstrates that HRV values changed significantly in response to physical work, indicating a change in the relative activity of cardiac autonomic functions as a result of fatigue. Using the ways in which HRV parameters vary in response to increased workloads provides a sensitive marker for contrasting construction workers with and without cardiovascular disease. It also allows the site manager to track how quickly workers fatigue, so that they can switch up their workload to reduce the likelihood that any one worker would get severely exhausted, or to suggest that workers who are already severely fatigued take a break to prevent further injury.

Detection of Respiratory Events during Sleep Based on Fusion Analysis and Entropy Features of Cardiopulmonary Signals.

Sleep apnea hypopnea syndrome (SAHS) is a common sleep disorder with a high prevalence. The apnea hypopnea index (AHI) is an important indicator used to diagnose the severity of SAHS disorders. The calculation of the AHI is based on the accurate identification of various types of sleep respiratory events. In this paper, we proposed an automatic detection algorithm for respiratory events during sleep. In addition to the accurate recognition of normal breathing, hypopnea and apnea events using heart rate variability (HRV), entropy and other manual features, we also presented a fusion of ribcage and abdomen movement data combined with the long short-term memory (LSTM) framework to achieve the distinction between obstructive and central apnea events. While only using electrocardiogram (ECG) features, the accuracy, precision, sensitivity, and F1 score of the XGBoost model are 0.877, 0.877, 0.876, and 0.876, respectively, demonstrating that it performs better than other models. Moreover, the accuracy, sensitivity, and F1 score of the LSTM model for detecting obstructive and central apnea events were 0.866, 0.867, and 0.866, respectively. The research results of this paper can be used for the automatic recognition of sleep respiratory events as well as AHI calculation of polysomnography (PSG), which provide a theoretical basis and algorithm references for out-of-hospital sleep monitoring.

Autonomic Nervous System Influences on Cardiovascular Self-Organized Criticality.

Cardiovascular self-organized criticality has recently been demonstrated. We studied a model of autonomic nervous system changes to better characterize heart rate variability self-organized criticality. The model included short and long-term autonomic changes associated with body position and physical training, respectively. Twelve professional soccer players took part in a 5-week training session divided into "Warm-up", "Intensive", and "Tapering" periods. A stand test was carried out at the beginning and end of each period. Heart rate variability was recorded beat by beat (Polar Team 2). Bradycardias, defined as successive heart rates with a decreasing value, were counted according to their length in number of heartbeat intervals. We checked whether bradycardias were distributed according to Zipf's law, a feature of self-organized criticality. Zipf's law draws a straight line when the rank of occurrence is plotted against the frequency of occurrence in a log-log graph. Bradycardias were distributed according to Zipf's law, regardless of body position or training. Bradycardias were much longer in the standing position than the supine position and Zipf's law was broken after a delay of four heartbeat intervals. Zipf's law could also be broken in some subjects with curved long bradycardia distributions by training. Zipf's law confirms the self-organized nature of heart rate variability and is strongly linked to autonomic standing adjustment. However, Zipf's law could be broken, the significance of which remains unclear.

0254 Diagnosis of Obstructive Sleep Apnea using Koopman Spectral Analysis

Abstract Introduction Koopman operator theory and the Hankel alternative view of the Koopman (HAVOK) model have been widely used to investigate the chaotic dynamics in complex systems. Although the statistics of intermittent dynamics have been evaluated in the HAVOK model, they are not adequate to characterize intermittent forcing. We proposed the Koopman spectral analysis method to characterize the intermittent transition between apeanic and non-apneaic episodes to predict impending OSA episodes. Methods Apnea-ECG Database from Physionet.org was selected to investigate the nonlinear intermittent behaviors of OSA disorder. The data set has 70 records of OSA patients, and the recordings vary from 7 hours to nearly 10 hours each. Each recording consists of a continuous digitized ECG signal and apnea annotations. First, the eigen time-delay coordinates were reconstructed from the heart rate variability (HRV) features. Next, the last eigen time-delay coordinate was modeled as an intermittent forcing component during apneic episodes. Finally, the forcing statistics in OSA were estimated to characterize the intermittency of the pathophysiological process. Results The statistics of the forcing in OSA demonstrated the fat-tailed distribution of the intermittent forcing of the underlying OSA pathophysiological process. The pooled means and standard deviations of the burst duration and the inter-burst duration across OSA patients were also calculated to be 1.73±0.26 minutes and 11.71±2.69 minutes. Scalogram amplitude and spectral decomposition of the wavelet transform exhibited various predominant frequencies and dynamics modes associated with apneic events. Conclusion We have reported the data-driven HAVOK model to obtain an intermittently forced linear system of chaos for the OSA pathophysiological processes The proposed method has been validated on 70 records of OSA patients from Apnea-ECG database. The forcing signal can predict the intermittent transient events, such as the transition from normal to apneic episodes in OSA case. Support (if any) Huynh PK, Setty AR, Le TB, Le TQ. A noise-robust Koopman spectral analysis of an intermittent dynamics method for complex systems: a case study in pathophysiological processes of obstructive sleep apnea. IISE Transactions on Healthcare Systems Engineering. 2022 Nov 1:1-6.

0417 Pre-sleep wakefulness in patients with insomnia: heart rate variability during sleep onset

Abstract Introduction Pre-sleep stress or hyperarousal is a known key etiological component in insomnia disorder. However, physiological alterations during the sleep onset period are not well-understood. We aimed to characterize heart rate variability (HRV) in patients with insomnia during sleep onset. Methods Using data from the Sleep Heart Health Study at National Sleep Research Resource, we conducted a secondary analysis exploring HRV during the sleep onset period (defined as the time between lights-off and sleep onset) in patients with insomnia (IS group; n = 68) and comorbid obstructive sleep apnea (IS + OSA group; n = 135), compared with normal sleep controls (NSC group; n = 122). Overall group differences were tested using ANOVA. Post-hoc pairwise comparisons were conducted using the least significant difference (LSD) tests. Results During sleep onset, mean heart rates of the IS group (74.4±15.0 beats/min) and IS+OSA group (72.6±11.5 beats/min) were both significantly higher than the NSC group (69.7±9.4 beats/min, p=0.007 and p=0.044 respectively). Both the IS and IS+OSA groups showed lower heart rate variability compared to the NSC group, with significant differences in both time and frequency domains of HRV analysis including SDNN (p=0.002), RMSSD (p=0.035), pNN20 (p&amp;lt; 0.001), pNN50 (p=0.008), LnLF (p&amp;lt; 0.001), LnHF (p&amp;lt; 0.001). For all these measures, IS+OSA group had lower HRV compared to the IS group, although differences were not significant. When controlling for age, gender and BMI, and compared to the NSC group, IS group had significantly higher heart rates (β=4.8, p=0.008), and the IS+OSA group had significantly lower pNN20 (β=-0.039, p=0.002), pNN50 (β=-0.031, p=0.017) and LnHF (β=0.007, p=0.007). Conclusion Patients with insomnia had lower vagal activity during the sleep onset period, reflected in higher heart rates and lower heart rate variability. Such altered heart rate dynamics may serve as a physiological biomarker for insomnia during bedtime wakefulness, or as a potential tool to evaluate the efficacy of behavioral interventions which target bedtime stress. Support (if any) This research project was made possible by a grant from the American Academy of Sleep Medicine Foundation (PI: Yan Ma). We acknowledge the National Sleep Research Resource as the resource through which the Data/Datasets were obtained.

СARDIAC ARRHYTHMIAS ANALYSIS BASED ON THE PHASE PORTRAIT OF ECG SIGNAL

The article illustrates the development of virtual system in LabVIEW software environment for processing and analysis of electrocardiographic signals. In the system, the issues of processing ECG signal with classical methods and in phase space have been considered, the comparison of these methods have been carried out, characteristic changes of the shapes of ECG phase portraits corresponding to the change of diagnostic signs haveb been analyzed, and additional diagnostic signs determined on the the base of phase portrait in order to increase the reliability of ECG diagnosis have been interpreted. As a result of the analysis of the sequence of RR intervals using the developed program, a histogram, a scatterogram, a fast Fourier transform, an autoregressive spectrum, a short-term Fourier transform and the corresponding informative parameters characterizing heart rate variability were obtained: the average length and standard deviation of RR intervals, the mean value and standard deviation heart rate, spectral parameters, etc. The results obtained during the processing and analysis of ECG files taken from the MIT BIH databases using the HRV analysis program and the determination of informative parameters implemented in the LabVIEW environment confirm the adequacy of the developed program. Keywords: ECG signal, phase space, phase portrait, RR intervals, LabVIEW software, heart rate, QRS complexes.

INDICATORS OF VEGETATIVE DYSFUNCTION IN ATHLETES WITH SIGNS OF OVERTRAINING

The article examines indicators of autonomic dysfunction and features of autonomic regulation of heart rhythm in athletes. The study was conducted on the basis of the sports medicine center of Sumy State University. Two groups of athletes took part in the study. The first group of athletes included those who had signs of a non-functional state and autonomic dysfunction (in the survey according to Wayne, or it was diagnosed with an unsatisfactory ortho/clinostatic reaction) - 27 people. The second group consisted of 40 people without signs of functional overstrain (overtraining). In the course of the study, highly qualified track and field athletes were examined using the method of heart rate variability (HRV), orthostatic and clinostatic tests, and Wayne's questionnaire. The main temporal, spectral and indicators of heart rate variability were studied, and the predominant type of autonomic regulation of heart activity was established. The study of indicators of vegetative regulation of the heart showed a statistical difference between indicators of heart rate variability in athletes. Thus, the study of the features of heart rate variability makes it possible to assess the state of the mechanisms of regulation of physiological functions in the human body, as well as the general activity of regulatory mechanisms, neurohumoral regulation of the heart, the relationship between sympathetic and parasympathetic links of the autonomic nervous system. It is also quite important that the obtained data on the athlete's functional state can be used in the future to evaluate the athlete's training process and ways to optimize it.

Robust PPG-Based Mental Workload Assessment System Using Wearable Devices.

Heart rate variability (HRV) has been used in assessing mental workload (MW) level. Compared with ECG, photoplethysmogram (PPG) provides convenient in assessing MW with wearable devices, which is more suitable for daily usage. However, PPG collected by smartwatches are prone to suffer from artifacts. Those signal corruptions cause invalid Inter-beat Intervals (IBI), making it challenging to evaluate the HRV feature. Hence, the PPG-based MW assessment system is difficult to obtain a sustainable and reliable assessment of MW. In this paper, we propose a pre- and post- processing technique, called outlier removal and uncertainty estimation, respectively, to reduce the negative influences of invalid IBIs. The proposed method helps to acquire accurate HRV features and evaluate the reliability of incoming IBIs, rejecting possibly misclassified data. We verified our approach in two open datasets, which are CLAS and MAUS. Experiment results show proposed method achieved higher accuracy (66.7% v.s. 74.2%) and lower variance (11.3% v.s. 10.8%) among users, which has comparable performance to an ECG-based MW system.

On the distinct differences in autonomic regulation between pregnant and non-pregnant women - a heart rate variability analysis

Objective. Appropriate adaptation of the maternal autonomic nervous system to progressing gestation is essential to a healthy pregnancy. This is partly evidenced by the association between pregnancy complications and autonomic dysfunction. Therefore, assessing maternal heart rate variability (HRV)—a proxy measure for autonomic activity—may offer insights into maternal health, potentially enabling the early detection of complications. However, identifying abnormal maternal HRV requires a thorough understanding of normal maternal HRV. While HRV in women of childbearing age has been extensively investigated, less is known concerning HRV during pregnancy. Subsequently, we investigate the differences in HRV between healthy pregnant women and their non-pregnant counterparts. Approach. We use a comprehensive suite of HRV features (assessing sympathetic and parasympathetic activity, heart rate (HR) complexity, HR fragmentation, and autonomic responsiveness) to quantify HRV in large groups of healthy pregnant (n = 258) and non-pregnant women (n = 252). We compare the statistical significance and effect size of the potential differences between the groups. Main results. We find significantly increased sympathetic and decreased parasympathetic activity during healthy pregnancy, along with significantly attenuated autonomic responsiveness, which we hypothesize serves as a protective mechanism against sympathetic overactivity. HRV differences between these groups typically had a large effect size (Cohen’s d > 0.8), with the largest effect accompanying the significantly reduced HR complexity and altered sympathovagal balance observed in pregnancy (Cohen’s d > 1.2). Significance. Healthy pregnant women are autonomically distinct from their non-pregnant counterparts. Subsequently, assumptions based on HRV research in non-pregnant women cannot be readily translated to pregnant women.

Real Time Risk Prediction of heart patients using HRV and IoT: A Survey

Abstract: In today's world, the leading cause of death is heart disease. The term heart disease is described as several different conditions that affect the heart. There are many types of heart disease, but the most common are coronary heart disease, congestive heart failure, and arrhythmia. Currently, the healthcare system is hospital essential which is inefficient to treatthese conditions, which require immediate assistance. This is a roundabout way to point to a rise in the death rates. Heart rate variability (HRV) is a useful tool for identifying and monitoring potential risk factors for heart disease. HRV measures the time between each heartbeat and can be used to identify subtle changes in heart rate that may be indicative of an underlying health condition. With the help of Machine learning and (Internet of Things) IoT-based heart patients real-time monitoring and predicting risk analysis will enable doctors to view patient's health status online. Internet of Things (IoT) technology to continuously track and monitor vital signs,including heart rate, pulse rate, and body temperature. In addition to these traditional vital signs, the system also includes afeature for monitoring heart rate variability (HRV), which has been shown to be a useful indicator of heart disease risk prediction. Using machine learning and HRV capabilities, the system will be able to assess health status and anticipate patient'sheart health (low-risk or high-risk) based on the ECG waveform and HRV characteristics such as time domain and frequency domain values

Features of Heart Rate Variability in Male and Female Wistar Rats

In this study, we investigated the heart rate variability (HRV) of awake male and female Wistar rats using a non-invasive technique of electrocardiosignal recording. We established significant differences in the value of HRV indicators in groups of animals differing in spectral parameters (total power of the spectrum (TP), high-(HF) and low-frequency (LF) components of the spectrum). The following HRV indicators were shown to be highly informative: statistical (SDNN — the standard deviation of the complete array of RR intervals, RMSSD — the square root of the standard deviations of successive RR intervals, CV — the coefficient of variation) and spectral (TP, HF, LF, VLF is a very low–frequency component of the HRV spectrum, LF/HF is the vagosympathetic interaction index, IC — centralization index). The data obtained should be taken into account when advancing the existing methodology for studying the pathogenetic mechanisms of HRV changes and determining the prognostic value of these HRV parameters.

Differential relationship of observer-rated and self-rated depression and anxiety scales with heart rate variability features.

Heart rate variability (HRV) is a known psychophysiological marker for diverse psychiatric symptoms. In this study, we aimed to explore the potential for clinical use of HRV by investigating the interrelationship between HRV indices and clinical measures mainly used to assess depressive and anxious symptoms. Participants who reported depressive and anxious symptoms were designated into the following groups: group 1, clinician-rated and self-rated depression; group 2, only self-rated depression; group 3, clinician-rated and self-rated anxiety; group 4, only self-rated anxiety. Statistical comparisons were performed between these groups to investigate the association between HRV and clinical measures. As a result, HRV variables showed significant correlations only with the clinician-rated assessments. Moreover, both time and frequency domain HRV indices were significantly different between groups 1 and 2, but groups 3 and 4 showed significant differences only in frequency domain HRV indices. Our study showed that HRV is an objective indicator for depressive or anxious symptoms. Additionally, it is considered a potential indicator for predicting the severity or state of depressive symptoms rather than of anxious symptoms. This study will contribute to increasing the diagnostic utility of discriminating those symptoms based on HRV in the future.

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

BackgroundSepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients.MethodsPatients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups.ResultsIn total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time.ConclusionsWe showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time.Trial registration The protocol of this study is published by Quinten et al., 2017.

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network.

Tetanus is a life-threatening infectious disease, which is still common in low- and middle-income countries, including in Vietnam. This disease is characterized by muscle spasm and in severe cases is complicated by autonomic dysfunction. Ideally continuous vital sign monitoring using bedside monitors allows the prompt detection of the onset of autonomic nervous system dysfunction or avoiding rapid deterioration. Detection can be improved using heart rate variability analysis from ECG signals. Recently, characteristic ECG and heart rate variability features have been shown to be of value in classifying tetanus severity. However, conventional manual analysis of ECG is time-consuming. The traditional convolutional neural network (CNN) has limitations in extracting the global context information, due to its fixed-sized kernel filters. In this work, we propose a novel hybrid CNN-Transformer model to automatically classify tetanus severity using tetanus monitoring from low-cost wearable sensors. This model can capture the local features from the CNN and the global features from the Transformer. The time series imaging - spectrogram - is transformed from one-dimensional ECG signal and input to the proposed model. The CNN-Transformer model outperforms state-of-the-art methods in tetanus classification, achieves results with a F1 score of 0.82±0.03, precision of 0.94±0.03, recall of 0.73±0.07, specificity of 0.97±0.02, accuracy of 0.88±0.01 and AUC of 0.85±0.03. In addition, we found that Random Forest with enough manually selected features can be comparable with the proposed CNN-Transformer model.