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

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

Soumanti Das (National Institute of Technology Rourkela, India), Suraj Kumar Nayak (National Institute of Technology Rourkela, India), Rohit Kumar Verma (National Institute of Technology Rourkela, India), Anilesh Dey (Kaziranga University, India) and Kunal Pal (National Institute of Technology Rourkela, India)
Copyright: © 2018 |Pages: 23
DOI: 10.4018/978-1-5225-5149-2.ch006

Abstract

In this chapter, the effect of an old generation romantic music (stimulus) on the autonomic nervous system (ANS) activity and the cardiac electrophysiology of Indian male volunteers was investigated. Electrocardiogram (ECG) signals were acquired and the corresponding RR intervals (RRIs) were extracted. The recurrence analysis of the RRI time series suggested a more stable heart rate in the post-stimulus condition. Heart rate variability (HRV) analysis detected a dominant parasympathetic activity in the post-stimulus condition. The time-domain and the wavelet transform analyses of the ECG signals predicted an alteration in the electrical activity of the heart because of the exposure to the music stimulus. The classification of the HRV and the ECG parameters was performed using artificial neural network (ANN), which resulted in an accuracy of ≥80%.
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Literature Review

The current section discusses some of the recent developments in the field of recurrence-based RRI time series analysis, HRV analysis, and wavelet-based joint time-frequency analysis of ECG signals. Schlenker et al. (2016) have proposed recurrence analysis as a tool for the diagnosis of the ANS dysfunction that is responsible for vasovagal syncope. ECG signals were recorded during orthostatic test from the patients, experiencing vasovagal syncope. Sequences of RRIs were analyzed using frequency-domain, time domain and recurrence analysis methods. Both time domain analysis and recurrence analysis showed significant changes in patients experiencing vasovagal syncope as compared to healthy patients. As the time domain HRV becomes problematic for short term ECG signal so the authors proposed that recurrence analysis can be considered as an important tool for detecting the ANS dysfunction in person having vasovagal syncope (Schlenker & Nedělka, 2013). Houshyarifar et al. (2017) have proposed a method to predict the sudden cardiac death, which is based on the features extracted from recurrence plot, poincarè plot of HRV signal. The method consisted of four stages, i.e., preprocessing, feature extraction, feature reduction and classification. Seven features were extracted from recurrence plot and poincarè plot. These features are then reduced to one feature by linear discrimination technique. Support vector machine (SVM) was used to classify the HRV signals. The proposed method could predict the occurrence of sudden cardiac death 5 min before the sudden cardiac death with an accuracy of 92% (Houshyarifar & Amirani, 2017).

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