Time-domain HRV Analysis of ECG Signal under Different Body Postures

Kumar, Prashant; Das, Ashis Kumar; Prachita,; Halder, Suman

doi:10.1016/j.procs.2020.03.435

Cited by 21 publications

(14 citation statements)

References 9 publications

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“…Time domain analysis is the simplest method among HRV parameters. It analyzes the R-R interval from a statistical point of view [12]. Geometric figures and statistical analysis are the two most commonly used analysis methods for HRV signals.…”

Section: Ecg Signal Feature Extractionmentioning

confidence: 99%

Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion

Chang

2021

Security and Communication Networks

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A multifeature fusion-based enterprise employee psychological stress prediction algorithm is suggested to address the concerns of low prediction accuracy, long duration, and poor results in current psychological stress prediction approaches. Examine ECG signal generation and properties, as well as the notion and causes of heart rate variability. The ECG signal is gathered according to the psychological stress reaction mechanism, and the digital filter is utilized to filter and preprocess the noise interference of the ECG signal. The linear discriminant analysis algorithm extracts the time domain linear features, frequency domain linear features, and nonlinear features of the ECG signal and then selects the ECG signal characteristics. D-S evidence theory is used to fuse the time domain linear characteristics, frequency domain linear characteristics, and nonlinear characteristics of the ECG signal, construct the psychological stress prediction model, obtain the final result of psychological stress state prediction, and realize the psychological stress prediction of enterprise employees, all based on multifeature fusion technology. The results of the experiments reveal that the suggested algorithm has a greater predictive effect on employee psychological stress, allowing it to enhance forecast accuracy and reduce prediction time.

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Section: Ecg Signal Feature Extractionmentioning

confidence: 99%

Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion

Chang

2021

Security and Communication Networks

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“…A similar study (10) examined HRV in the supine, sitting, and standing positions, which was higher in the standing position than in the sitting and supine positions. This study also concluded that heart rate is low in the supine position and starts to increase with the change in position to sitting and then standing, they also found significant differences in several components of HRV between postures.…”

Section: Discussionmentioning

confidence: 96%

“…There are several parameters used for R-R interval time series analysis, including time domain and frequency domain as the standard analysis for HRV by the European Society of Cardiology Parameters Task Force (8,9) . In recent years, numerous studies have used HRV as a suitable ANS modulation biomarker (10) . Examine the ANS in different positions: supine, sitting, and then standing, Also, (6) supine and standing positions were used to investigate the ANS.…”

Section: Introductionmentioning

confidence: 99%

Linear Analysis of ECG Data Variability to Assess The Autonomic Nervous System in Two Different Body Positions

Farhan¹,

Lyazidi²,

Elkettani³

et al. 2023

The Egyptian Journal of Hospital Medicine

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Background: Heart rate variability (HRV), generated from an electrocardiogram (ECG), can be used to evaluate the autonomic nervous system (ANS). Heart rate changes during different physical activities are due to changing involuntary regulation. Objective: The main aim of the current study is to explore the ANS by analyzing linear HRV parameters, so that clinicians can preliminarily explore ANS function using ECG analysis with their patients. Patients and methods: A total of 15 student volunteers were recorded. Data was collected for 5 minutes for each supine and standing position. The R-peak obtained from an ECG is used to assess the RR-peak, which is also required for HRV analysis. Linear HRV parameters with different time-domain indices and frequency-domain indices are interpreted in 2 body positions. Results: We discovered that the RR interval is longer in the supine position than in the standing position (952.8±181.6 vs. 771.9±164, respectively; P<0.05), and that in comparison to supine position, when standing as opposed to when lying down, the heart rate is higher (81.7±10.2 vs. 65.8±9.1, respectively; P<0.05), where the body is more relaxed. This affects ANS, and stress index values, which are low before increasing in the standing position, starting in the supine position (5.7±2.4 vs. 9.5±2.7, respectively; P<0.05). Conclusion:We conclude that there is a relationship between HRV and the sympathetic system in different positions, where when moving from supine to standing, the values of HRV changes (increases) and, therefore, the stress index changes (increase) and, from this, we note the change (value increase) in ANS.

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“…In general, HRV can be analyzed either in the time domain or the frequency domain [ 35 , 36 ]. In this study, we pursued time domain analysis as it (1) facilitates translation from the ECG-derived metrics to the MCG-derived ones, (2) empowers evaluation with limited recording time and (3) has been widely used in prior works [ 37 ]. ECG-based frequency domain analysis [ 38 ] has found correlations between the oscillatory components within different ECG frequency bands and different levels of cognitive workload.…”

Section: Methodsmentioning

confidence: 99%

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

Wang

Zhu

Kaur

et al. 2022

Sensors

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Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.

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Time-domain HRV Analysis of ECG Signal under Different Body Postures

Cited by 21 publications

References 9 publications

Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion

Research on Prediction Algorithm of Employees’ Psychological Stress Based on Multifeature Fusion

Linear Analysis of ECG Data Variability to Assess The Autonomic Nervous System in Two Different Body Positions

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

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