Hybrid Deep Learning Model for Sleep Disorders Detection

The polysomnography test (sleep study) is used to diagnose several sleeping disorders. Sleep study is used to detect sleep disorders such as Insomnia, REM Sleep Behavior, Insomnia, Restless Leg Movement Syndrome, and Sleep Apnea. It measures different parameters such as heart rate, level of oxygen in your blood, body position, brain waves (EEG), breathing rate, eye movement, and electrical activities of muscles. In the world, 700 million people suffer from sleeping disorders. A wide range of sensors was attached to the body of the patient to measure the value of different parameters. However, in 2020, due to the exponential spread of COVID-19 coronavirus disease, the sleep study centers were closed, and it was very difficult to perform sleep studies on patients. Therefore, we developed a hybrid model based on deep learning techniques like Convolutional Neural Network (CNN) and Deep Belief Network (DBN) architectures. Numerous cameras were mounted in rooms at certain angles, which provide live surveillance data and record a patient’s movements after a short periodic interval of time. This research paper concludes that non-contact-based hybrid models are highly accurate in detecting sleep disorders based on polysomnography tests.

Keywords: Brain Waves, Convolutional Neural Network (CNN), Covid-19, Deep Belief Network, Physiological electrical signals, Sleep disorders.