Effect of a Motivational Song on the Autonomic Nervous System and the Heart of Indian Male Volunteers

Effect of a Motivational Song on the Autonomic Nervous System and the Heart of Indian Male Volunteers

Gitika Yadu (National Institute of Technology Rourkela, India), Suraj Kumar Nayak (National Institute of Technology Rourkela, India), Debasisha Panigrahi (National Institute of Technology Rourkela, India), Sirsendu Sekhar Ray (National Institute of Technology Rourkela, India) and Kunal Pal (National Institute of Technology Rourkela, India)
Copyright: © 2018 |Pages: 19
DOI: 10.4018/978-1-5225-4969-7.ch015
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This chapter investigates the effect of a motivational song (stimulus) on the physiology of the autonomic nervous system and the electrical activity of the heart. Five min electrocardiogram (ECG) signals were acquired from 19 volunteers during the resting and the post-stimulus conditions. The RR intervals (RRIs) were extracted. Recurrence analysis of the RRI time series indicated a higher alteration (acceleration or deceleration) in the heart rate along with the reduction of the causality and patterned behavior of the RRIs. The exact alteration in the ANS physiology was examined using heart rate variability (HRV) analysis. The results of the HRV analysis suggested an increase in the parasympathetic activity in the post-stimulus condition. The alteration in the cardiac activity was analyzed using time domain and joint time-frequency domain analyses of ECG signals. The results suggested an alteration in the cardiac electrical activity of the heart in the post-stimulus condition.
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In the last few decades, the effect of music on the different human physiological systems has been extensively studied. Elliot et al. (2005) have reported that the exercise intensity, in-task effect and positive attitude towards exercise can be improved if the volunteers are made to listen to motivational music (Elliot, 2005). Recurrence analysis has evolved as an important non-linear tool for the analysis of different time series including the cardiovascular signals like the RRI series and the ECG signals (H Sabelli et al., 2005). Sun et al. (2008) have used recurrence analysis of RRIs for predicting the termination of atrial fibrillation, which can be useful to understand the mechanisms of arrhythmia (Sun & Wang, 2008). Sabelli et al. (2011) reported recurrence analysis-based estimation of distinct creative patterns in RRIs of depressed and psychotic persons (H Sabelli, Messer, Kovacevic, & Walthall, 2011). Chen et al. (2014) have proposed the use of recurrence analysis of HRV signals for quantifying the variation in the cardiovascular activity when exposed to low-frequency noise of a range of intensities (Chen et al., 2014). HRV analysis is an important non-invasive method to understand the ANS activity. Xue et al. (2015) have implied time domain analysis on RRIs to analyse the features of HRV. The statistical features frequently used for the RRIs were difference between the square root mean square, overall standard deviation and mean (Xue, Chen, Fang, & Xia, 2015).

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