scholarly journals A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-Min Chung ◽  
Chuan-Shen Hu ◽  
Yu-Lun Lo ◽  
Hau-Tieng Wu
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Algebraic Topology ◽  
Persistent Homology ◽  
Heart Rate Variability Analysis ◽  
Analysis Tool ◽  
Classification Problems ◽  
Hrv Analysis ◽  
Persistence Diagrams

Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects—the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.

scholarly journals Fast time series detrending with applications to heart rate variability analysis

2019 ◽  
Vol 30 (09) ◽  
pp. 1950069
Author(s):  
M. Andrecut
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Diffusion Equation ◽  
Time Series Analysis ◽  
Heart Rate Variability Analysis ◽  
Fast Time ◽  
Variability Analysis ◽  
Long Time ◽  
Hrv Analysis

In this paper, we discuss a new fast detrending method for the nonstationary RR time series used in Heart Rate Variability (HRV) analysis. The described method is based on the diffusion equation, and we show numerically that it is equivalent to the widely used Smoothing Priors Approach (SPA) and Wavelet Smoothing Approach (WSA) methods. The speed of the proposed method is comparable to the WSA method and it is several orders of magnitude faster than the SPA method, which makes it suitable for very long time series analysis.

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

scholarly journals Noninvasive method for electrocardiogram recording in conscious rats: feasibility for heart rate variability analysis

2010 ◽  
Vol 82 (2) ◽  
pp. 431-437 ◽  
Author(s):  
Pedro P. Pereira-Junior ◽  
Moacir Marocolo ◽  
Fabricio P. Rodrigues ◽  
Emiliano Medei ◽  
José H.M. Nascimento
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Conscious State ◽  
Heart Rate Variability Analysis ◽  
Platinum Electrodes ◽  
Noninvasive Method ◽  
Conscious Rats ◽  
Custom Made ◽  
Surface Ecg ◽  
Hrv Analysis

Heart rate variability (HRV) analysis consists in a well-established tool for the assessment of cardiac autonomic control, both in humans and in animal models. Conventional methods for HRV analysis in rats rely on conscious state electrocardiogram (ECG) recording based on prior invasive surgical procedures for electrodes/transmitters implants. The aim of the present study was to test a noninvasive and inexpensive method for ECG recording in conscious rats, assessing its feasibility for HRV analysis. A custom-made elastic cotton jacket was developed to fit the rat's mean thoracic circumference, with two pieces of platinum electrodes attached on its inner surface, allowing ECG to be recorded noninvasively in conscious, restrained rats (n=6). Time- and frequency-domain HRV analyses were conducted, under basal and autonomic blockade conditions. High-quality ECG signals were obtained, being feasible for HRV analysis. As expected, mean RR interval was significantly decreased in the presence of atropine (p <0.05) and increased in the presence of propranolol (p<0.001). Also, reinforcing the reliability of the method, low- and high-frequency HRV spectral powers were significantly decreased in the presence of propranolol (p <0.05) and atropine (p< 0.001), respectively. In summary, the present work describes a novel, inexpensive and noninvasive method for surface ECG recording in conscious rats.

scholarly journals Persistent homology as a new method of the assessment of heart rate variability

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253851
Author(s):  
Grzegorz Graff ◽  
Beata Graff ◽  
Paweł Pilarczyk ◽  
Grzegorz Jabłoński ◽  
Dariusz Gąsecki ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Data Analysis ◽  
Persistent Homology ◽  
Assessment Tools ◽  
Topological Data Analysis ◽  
Topological Descriptors ◽  
Time Interval ◽  
Analysis Tool ◽  
Frequency Domain Methods

Heart rate variability (hrv) is a physiological phenomenon of the variation in the length of the time interval between consecutive heartbeats. In many cases it could be an indicator of the development of pathological states. The classical approach to the analysis of hrv includes time domain methods and frequency domain methods. However, attempts are still being made to define new and more effective hrv assessment tools. Persistent homology is a novel data analysis tool developed in the recent decades that is rooted at algebraic topology. The Topological Data Analysis (TDA) approach focuses on examining the shape of the data in terms of connectedness and holes, and has recently proved to be very effective in various fields of research. In this paper we propose the use of persistent homology to the hrv analysis. We recall selected topological descriptors used in the literature and we introduce some new topological descriptors that reflect the specificity of hrv, and we discuss their relation to the standard hrv measures. In particular, we show that this novel approach provides a collection of indices that might be at least as useful as the classical parameters in differentiating between series of beat-to-beat intervals (RR-intervals) in healthy subjects and patients suffering from a stroke episode.

scholarly journals Autonomic Cardiac Activity in Patients with Smoking and Alcohol Addiction by Heart Rate Variability Analysis

2016 ◽  
Vol 39 (6) ◽  
pp. 147 ◽  
Author(s):  
Ramazan Yuksel ◽  
Rabia Nazik Yuksel ◽  
Tijen Sengezer ◽  
Senol Dane
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Cardiac Rhythm ◽  
Cardiac Activity ◽  
Alcohol Addiction ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Dsm Iv ◽  
Hrv Analysis ◽  
Almost All

Purpose: Smoking and alcohol addictions are common and worldwide. In the present study, we aimed to investigate the effects of these addictions on cardiac rhythm using heart rate variability (HRV) analysis. Methods: Addicts (n=42 men: 22 cigarette; 20 cigarette and alcohol) and age-matched controls (n=34 men) were included in the study. All patients fulfill the criteria for dependence according to DSM-IV-TR. Electrocardiography (ECG) recordings were obtained for a total of 30 minutes. Fagerstrom Nicotine Addiction Test (FNAT) and CAGE questionnaire (Cut down, Annoy, Guilt, Eye opener) was applied to all patients. Results: Almost all HRV parameters were significantly decreased in cigarette and cigarette and alcohol addicts compared with controls (p

scholarly journals Validity of the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition and Fitness Level Groups

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 902
Author(s):  
Adrián Hernández-Vicente ◽  
David Hernando ◽  
Jorge Marín-Puyalto ◽  
Germán Vicente-Rodríguez ◽  
Nuria Garatachea ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Body Composition ◽  
Excellent Agreement ◽  
Exercise Intensity ◽  
Heart Rate Variability Analysis ◽  
Fat Percentage ◽  
Fitness Level ◽  
Hrv Analysis ◽  
Frequency Power

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried out to evaluate how these phenotypic characteristics influenced HR and HRV measurements. For this purpose, sixty-seven volunteers performed a test consisting of the following consecutive segments: sitting rest, three submaximal exercise intensities in cycle-ergometer and sitting recovery. The agreement between HRV indices derived from Polar H7 and a simultaneous electrocardiogram (ECG) was assessed using concordance correlation coefficient (CCC). The percentage of subjects not reaching excellent agreement (CCC > 0.90) was higher for high-frequency power (PHF) than for low-frequency power (PLF) of HRV and increased with exercise intensity. A cluster of unfit and not young volunteers with high trunk fat percentage showed the highest error in HRV indices. This study indicates that Polar H7 and ECG were interchangeable at rest. During exercise, HR and PLF showed excellent agreement between devices. However, during the highest exercise intensity, CCC for PHF was lower than 0.90 in as many as 60% of the volunteers. During recovery, HR but not HRV measurements were accurate. As a conclusion, phenotypic differences between subjects can represent one of the causes for disagreement between HR sensors and ECG devices, which should be considered specifically when using Polar H7 and, generally, in the validation of any HR sensor for HRV analysis.

scholarly journals Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3870 ◽  
Author(s):  
Keisuke Kamata ◽  
Koichi Kinoshita ◽  
Manabu Kano
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Least Squares ◽  
Partial Least Squares ◽  
Health Monitoring ◽  
Normal Sinus Rhythm ◽  
Local Model ◽  
Heart Rate Variability Analysis ◽  
Modeling Framework ◽  
Hrv Analysis

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

scholarly journals Heart Rate Variability Analysis in Risk of Asthma Stratification

2017 ◽  
Vol 5 ◽  
Author(s):  
Javier Milagro ◽  
Eduardo Gil ◽  
Jesús Lázaro ◽  
Ville-Pekka Seppä ◽  
L. Pekka Malmberg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Early Diagnosis ◽  
Heart Rate Variability Analysis ◽  
Long Term Effects ◽  
Variability Analysis ◽  
Hrv Analysis

Early diagnosis of asthma is crucial to avoid long-term effects such as permanent airway obstruction. Pathogenesis of asthma has been related with autonomic nervous system (ANS) dysfunction, concretely with abnormal parasympathetic activity. As heart rate variability (HRV) analysis does reflect ANS activity, it has been employed here in risk of asthma stratification.

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