Deep Learning-based Mental Health Monitoring Scheme for College Students Using Convolutional Neural Network

Artificial intelligence (AI) in healthcare has recently been promising using deep neural networks. It is indeed even been in clinical trials more and more, with positive outcomes. Deep learning is the process of using algorithms to train a neural network model using huge quantities of data to learn how to execute a given task and then make an accurate classification or prediction. Apart from physical health monitoring, such deep learning models can be used for the mental health evaluation of individuals. This study thus designs a deep learning-based mental health monitoring scheme (DL-MHMS) for college students. This model uses the most efficient convolutional neural network (CNN) to classify the mental health status as positive, negative, and normal using the EEG signals collected from college students. The simulation analysis achieves the highest classification accuracy and F1 scores of 97.54% and 98.35%, less sleeping disorder rate of 21.19%, low depression level of 18.11%, reduced suicide attention level of 28.14%, increasing personality development ratio of 97.52%, enhance self-esteem ratio of 98.42%, compared to existing models.