Chin EMG Scalogram-Based Deep CNN for OSA Screening

Obstructive Sleep Apnea (OSA) is a common sleep condition characterized by frequent pauses in breathing caused by the relaxation of muscles in the upper airway during sleep. These pauses manifest in changes observed in Chin Electromyography (EMG), airflow, and oxygen saturation signals. In this paper, we propose a deep convolutional neural network (DCNN) architecture for screening OSA events and normal breathing for the OSA subjects. We utilized data from 5 OSA subjects from the American Center for Psychiatry and Neurology (ACPN) database. In this paper, we achieved a validation accuracy of 80% and a testing accuracy of 75%. Additionally, we investigated the firing pattern of motor neurons for both OSA events and non-OSA events. It was observed that for OSA subjects, the firing pattern is extremely low during OSA events, indicating muscle relaxation, while for non-OSA events, activity is high throughout the entire duration. This proposed system offers easy discrimination between OSA and non-OSA events, facilitating prompt treatment for OSA patients.