Music Therapy Methods Based on SVM and MLP

Abstract

Lately, the music therapy is being used widely around the world. Henceforth, this study focuses on the analysis of the music’s effect on fetal heart rate (FHR) curve. To this end, we treated people with music therapy and carried out the experiment. Firstly, 118 people with 32–40 weeks of gestational age who were expecting to give birth were invited to participate in the experiment. There was one control group and three experimental groups: 27 people were in the control group, 32 people were in the experimental group that listened to music for the first 10 minutes and did not listen to music for the second 10 minutes, 29 people were in the experimental group that did not listen to music for the first 10 minutes and listened to music for the second 10 minutes, and 30 people were in the experimental group that listened to music for all 20 minutes. In this paper, a convolutional neural network (CNN) based data processing model for fetal heart rate curves is proposed to improve the accuracy of fetal status assessment. First, the model method divides the high-dimensional one-dimensional fetal heart rate (FHR) records into 10 segments and then the characteristics of the FHR are extracted using a feature extraction method based on basic statistics. These features are regarded as input of support vector machine (SVM) and multilayer perceptron (MLP) for classification. According to the experimental results, the classification accuracies of SVM and MLP are 85.98% and 93.24%, respectively.