Statistical heart rate variability analysis under rest and post-exercise. (7th April 2022)

• Record Type: Journal Article
• Authors: Kumar, Prashant; Das, Ashis Kumar; Halder, Suman
• Is Part Of: International journal of computational science and engineering. Volume 25:Number 2(2022)
• Page Start: 186
• Page End: 197
• Publication Date: 2022-04-07
• Subjects: electrocardiogram -- ECG -- heart rate variability -- HRV -- heart rate -- Poincare plot -- approximate entropy -- ApEn -- standard error of the mean -- SEM -- coefficient of variation -- analysis of variance -- ANOVA; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Heart rate variability (HRV) analysis indicates the physical occurrence of variation of inter-beat interval and can be employed for regular monitoring of the health of the sportsperson. For the present work, 62 datasets (two from each participant) have been incorporated who were actively engaged in some sort of morning exercise or games to investigate about chronic fatigue and underperformance due to overtraining. Data were acquired by using BIOPAC MP45 and pre-processed signals have been applied for R peak detection using maximum overlap discrete wavelet transform (MODWT). Analysis of variance (ANOVA) and Wilcoxon signed-rank test have been evaluated to differentiate HRV parameters in both resting and post-exercise conditions. The p-value based on ANOVA for each HRV indices suggests that there is no statistically significant difference between both sets of data and it accedes to the null hypothesis but significant differences have been attained for the standard deviation of heart rate and approximate entropy in the case of the Wilcoxon signed-rank test. The statistically significant difference in resting and post-exercise data may be due to overtraining involved during exercise which is a very common issue for athletes and sportspersons. Overtraining can be monitored with the help of biosignals non-invasively.