Investigating the Impact of Musical Therapy on Physiological Stress in College Students Using Mixed Density Neural Networks

Abstract

This study examines the impact of musical therapy on psychotherapy conditions such as stress in college students. College students encounter numerous stressors including academic pressure and social challenges. It negatively impacts their physical and mental well-being which leads to anxiety and depression. Musical therapy has been recognized as a tool for stress reduction. However, the mechanisms underlying its effectiveness remain unclear. Therefore, this research utilizes Mixed Density Neural Networks (MDNN) to analyze the physiological responses associated with musical therapy. The initial phase focuses on collecting multi-modal data both with and without music. This data is collected from college students using surveys and physiological sensors. In the second phase data is preprocessed to remove noise and anomalies which is then followed by feature extraction which captures relevant information from the signals. In the third phase, the collected data is analyzed using MDNN, capable of handling both continuous and categorical data. It has an input layer, two hidden layers, a mixed-density layer, and an output layer. The input layer uses a linear activation function to process data from physiological sensors and musical stimuli features. The first and second hidden layer uses ReLU activation functions and has 50 and 25 neurons respectively. The mixed-density layer has one neuron and uses a sigmoid activation function for adaptive connection density based on input data. Finally, the output layer has one neuron and a linear activation function to predict stress levels. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to assess the performance of the model. It shows that slow and calming music significantly reduces stress levels among college students. Moreover, the implementation of the proposed algorithm improved the accuracy of stress level predictions by 20% and outperformed its predecessors.