Purpose: Several studies have been done measuring the effects of music on various vital signs more frequently on the electrocardiogram (ECG) and consequently the heart rate (HR). This study has been conducted to address the effects of Iranian music on cardiac functioning by thoroughly examining the extracted HR from ECG signals. A strong mathematical method is needed to extract signal features. One of the adaptive mathematical analyses is empirical mode decomposition (EMD), which is implemented to analyze the nonlinear and non-stationary data. This method can decompose any complicated signal into a group of intrinsic mode functions (IMFs) through a sifting process. Basic methods: In this paper the EMD-based feature extraction algorithm of HR signal which does not require a priori functional basis will be described. Fast Fourier transforms (FFT) are used to identify the peaks in the signal. Then maximum amplitude (MaxFFT) and maximum frequency (MaxFreq) using FFT and sample entropy (SampEn) for each extracted IMF and their combinations are calculated. SampEn algorithm is applied to calculate the complexity of each IMF and their combinations. Paired sample t-test was also conducted to assess if there were any significant differences between MaxFFT, SampEn and MaxFreq values of the IMFs. Main results: Considering the high frequency IMFs, results indicate that the MaxFFT values are decreased, but the SampEn and MaxFreq values are increased during listening to Iranian music. Conclusion: Experimental results from 62 subjects showed that the proposed methodology can be useful to show the differences between pre-music and during-music stages.


Empirical Mode Decomposition, Fast Fourier Transform, Heart Rate (HR), Iranian Music, Sample Entropy