Understanding the physiological effect of a motivational song on the heart and the autonomic nervous system of male volunteers by ECG and RR interval signal processing and analysis

Abstract

Listening to a song has been reported to evoke the emotion of the persons. In fact, it acts as a stimulus, which can initiate emotional memories in the person. Recent studies have suggested that the alteration in the emotions while listening tosong is also associated with a change in the physical state of the person. Listening to music has been reported to alter the physiological processes like promoting sleep, reduce anxiety, increase positive post-task attitude towards exercise and/or increase brain activities. The electrical activity of the heart can be analysed non-invasively using electrocardiogram (ECG) signals.Classification of ECG signals has been extensively studied by the researchers for the diagnosis of cardiac diseases as well as for identifying any alteration in the cardiac electrophysiology due to a stimulus.ECG signals were acquired from 18 healthy male volunteers during the pre-and the post-stimulus conditions.The RR intervals (RRIs) were extracted.The processing of RRI time series was performed to understand the autonomic nervous system (ANS) physiology in the volunteers.Recurrence analysis of both the ECG and the RRI signals indicated a higher alteration (acceleration or deceleration) in the heart rate along with the reduction of the causality and patterned behavior of the RRIs when the stimulus was applied.The ECG and the RRI signals were also processed using wavelet packet decomposition (WPD)methods.WPD was conducted on Daubechies wavelet (db04). Statistically important parameters were identified from the extracted parameters using t-test, Classification and Regression Tree (CART), Boosted Tree (BT) and Random Forest (RF) methods. Radial basis function (RBF) and multilayer perceptron (MLP) Artificial Neural Networks(ANNs) were implemented for the classification of the ECG and the RRI signals. In this study, the RBF network proved to be a better classifier than the MLP network, and it resulted in a classification efficiency of ≥80%, suggesting an alteration in the cardiac electrophysiology of the volunteers caused by the stimulus.