Complexity and Information-Based Analysis of the Heart Rate Variability (Hrv) While Sitting, Hand Biking, Walking, and Running

Namazi, Hamidreza

doi:10.1142/s0218348x21502017

Cited by 16 publications

(1 citation statement)

References 43 publications

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“…Cardiovascular disease not only occurs in the elderly but also tends to be younger ( 2 ). Heart rate variability (HRV) refers to the variation of heartbeat cycle and is a research hotspot in electrocardiogram (ECG) signal processing field in recent years ( 3 ). It contains information about the regulation of neurohumoral factors in the cardiovascular system and is a specific quantitative indicator reflecting the regulation of autonomic nerves in the heart ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of heart rate variability parameter and mental stress index

Luo

Zhang²,

Su³

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCardiovascular disease not only occurs in the elderly but also tends to become a common social health problem. Considering the fast pace of modern life, quantified heart rate variability (HRV) indicators combined with the convenience of wearable devices are of great significance for intelligent telemedicine. To quantify the changes in human mental state, this article proposes an improved differential threshold algorithm for R-wave detection and recognition of electrocardiogram (ECG) signals.MethodsHRV is a specific quantitative indicator of autonomic nerve regulation of the heart. The recognition rate is increased by improving the starting position of R wave and the time-window function of the traditional differential threshold method. The experimental platform is a wearable sign monitoring system constructed based on body area networks (BAN) technology. Analytic hierarchy process (AHP) is used to construct the mental stress assessment model, the weight judgment matrix is constructed according to the influence degree of HRV analysis parameters on mental stress, and the consistency check is carried out to obtain the weight value of the corresponding HRV analysis parameters.ResultsExperimental results show that the recognition rate of R wave of real-time 5 min ECG data collected by this algorithm is >99%. The comprehensive index of HRV based on weight matrix can greatly reduce the deviation caused by the measurement error of each parameter. Compared with traditional characteristic wave recognition algorithms, the proposed algorithm simplifies the process, has high real-time performance, and is suitable for wearable analysis devices with low-configuration requirements.ConclusionOur algorithm can describe the mental stress of the body quantitatively and meet the requirements of application demonstration.

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Section: Introductionmentioning

confidence: 99%

Quantitative analysis of heart rate variability parameter and mental stress index

Luo

Zhang²,

Su³

et al. 2022

Front. Cardiovasc. Med.

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Resilience characterized and quantified from physical activity data: A tutorial in R

Baretta

Koch

Cobo

et al. 2023

Psychology of Sport and Exercise

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Investigation of the Effect of Walking Speed on Leg Muscle Reaction by Complexity-Based Analysis of Electromyogram (Emg) Signals

Namazi

Pakniyat²

2021

Fractals

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An important research area in physiological and sport sciences is the analysis of the variations of the muscle reaction due to changes in walking speed. In this paper, we investigated the effect of walking speed variations on leg muscle reaction by the analysis of Electromyogram (EMG) signals at different walking inclines. For this purpose, we benefited from fractal theory and sample entropy to analyze how the complexity of EMG signals changes at different walking speeds. According to the results, although fractal theory could not show a clear trend between the variations of the complexity of EMG signals and the variations of the walking speed, however, based on the results, increasing the speed of walking in the case of different inclines is mapped on to the decrement of the sample entropy of EMG signals. Therefore, sample entropy could decode the effect of walking speed on the reaction of leg muscle. This analysis method could be applied to analyze the variations of other physiological signals of humans durin walking.

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Complexity and Information-Based Analysis of the Heart Rate Variability (Hrv) While Sitting, Hand Biking, Walking, and Running

Cited by 16 publications

References 43 publications

Quantitative analysis of heart rate variability parameter and mental stress index

Quantitative analysis of heart rate variability parameter and mental stress index

Resilience characterized and quantified from physical activity data: A tutorial in R

Investigation of the Effect of Walking Speed on Leg Muscle Reaction by Complexity-Based Analysis of Electromyogram (Emg) Signals

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