Abstract
Listening to music has been reported to provide health benefits. This has inspired the researchers to recognize the effect of music on various organs like the heart. In the past few decades, analysis of the electrocardiogram (ECG) signals has been widely used to divulge information about the cardiac activity not only for the diagnosis of the cardiovascular diseases but also during the exposure to a stimulus like music. This study attempts to identify the occurrence of any change in the cardiac activity due to the exposure to a motivational song. The ECG signals were acquired before and after exposing 18 volunteers to the motivational song. The recurrence plot analysis and recurrence quantification analysis (RQA) of the ECG signals were performed. The statistical analysis of the RQA features suggested a variation in the cardiac activity, which was further evinced by the classification of the RQA features using the artificial neural network (ANN) with an accuracy of >85%.
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Paul, S., Yadu, G., Nayak, S.K., Dey, A., Pal, K. (2020). Recurrence Quantification Analysis of Electrocardiogram Signals to Recognize the Effect of a Motivational Song on the Cardiac Electrophysiology. In: Maharatna, K., Kanjilal, M., Konar, S., Nandi, S., Das, K. (eds) Computational Advancement in Communication Circuits and Systems. Lecture Notes in Electrical Engineering, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-13-8687-9_16
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DOI: https://doi.org/10.1007/978-981-13-8687-9_16
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