Artificial intelligence driven mental health diagnosis based on physiological signals - P.R. Naregalkar; A.A. Shinde; M.V. Patil (MethodsX)
Mental health disorders like stress, anxiety, and depression are increasing rapidly these days. Diagnosis of mental health disorders is a matter of concern in this era. A cost-effective and efficient method is to be implemented for detection. With this aim, stress is being monitored in this work with the help of physiological signals. This work uses a machine learning approach to detect a subject in stressed and non-stressed situations. This work aims to automatically detect stressful situations in humans using physiological data collected during anxiety-inducing scenarios. Diagnosing stress in the early stage can be helpful to minimize the risk of stress-related issues and enhance the overall well-being of the patient. The traditional methods for diagnosing stress are based on patient reporting. This approach has limitations. This proposed research aims to develop a stress-assessing model with a machine learning approach. • Stress and anxiety these days have become a prevalent issue affecting individuals' well-being and productivity. Early detection of these conditions is crucial for timely intervention and prevention of associated health complications. This paper presents a machine learning model for stress diagnosis. • The dataset consists of recordings obtained from individuals under different stress levels. The physiological signals used in this project are ECG, EMG, HR, RESP, Foot GSR, and Hand GSR. The machine learning algorithms, like Decision tree and kernel support vector machine, are employed for dope classification tasks. Additionally, a deep learning framework based on feed-forward artificial neural networks is introduced for comparative analysis. • The study evaluates the accuracies of both binary (Stressed Vs. non-Stressed) and three-class (relaxed Vs. baseline Vs. stressed) classification. Results demonstrate promising accuracies with machine learning techniques achieving up to 91.87 % and 66.68 % for binary classes and three classifications respectively. This paper highlights the potential of machine learning methods accurately detecting mental disorders offering insights for the development of effective detection managing tools.