Heart Rate Variability Characterization in Daily Physical Activities Using Wavelet Analysis and Multilayer Fuzzy Activity Clustering

Abstract

A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).

Wavelet coherence reveals entrainment of heart rate variability among people involved in group activities

“…3 we represent the wavelet coherence between subject S 1 and subject S 2 during the different activities of yoga session (Y1) 3 , computed using Eq. (8). We divide the time into activities and frequencies into VLF, LF, and HF as mentioned in Section I.…”

“…Many signal processing methods have been proposed to distinguish among the causes of HRV and to extract relevant information. A natural choice to study a non-stationary signal in the frequency domain is the use of wavelet analysis, as proposed in [8].…”

Exploring an evolutionary medical analytic wallet

“…Previous works [1][2][3][5][6][7] presented several methods such as an analytic wallet, computer aided diagnosis, clustering and matching algorithms to support information based and translational medicine. This paper expands the notion of an electronic health record to a fully integrated biomedical analytic environment.…”

Towards a real-time application to reveal entrainment among people

“…It is not clear what is producing the oscillations at VLF; it may be related to long-term fluctuations in the thermoregulatory system or regulation of blood pressure and water balance. Although many signal processing techniques have been proposed to study these kind of nonstationary signals, a natural choice to study them in the frequency domain, adopted in this paper, is the use of wavelet analysis, as proposed in [10].…”