Abedi Behzad, Abbasi Ataollah, Goshvarpour Atefeh
Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology; Tabriz-Iran.
Anatol J Cardiol. 2017 May;17(5):398-403. doi: 10.14744/AnatolJCardiol.2016.7436. Epub 2017 Jan 17.
In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women.
Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music.
Collected data were separated into two data sets: train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively.
The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.
在过去几十年里，多项研究报告了听音乐的生理效应。不同类型的音乐对不同人的生理效应有所不同。在本研究中，我们旨在探究聆听传统波斯音乐对年轻女性心电图（ECG）信号的影响。
22名健康女性参与了本研究。在休息和听音乐两种条件下记录心电图信号。对于每个心电图信号，选取了20个基于形态学和小波的特征。使用人工神经网络（ANN）和概率神经网络（PNN）分类器对听音乐期间和听音乐之前的心电图信号进行分类。
收集的数据被分为两个数据集：训练集和测试集。在训练数据集中，使用ANN和PNN分别达到了88%和97%的分类准确率。此外，测试数据集用于评估分类器，使用ANN和PNN分别获得了84%和93%的分类率。
本研究基于小波变换和形态学特征探究了音乐对心电图信号的影响。这里获得的结果可为研究人员提供关于音乐对心电图信号影响的良好理解。
