The development of wearable, intelligent, and real-time monitoring of biological signals is gaining significant attention, especially in precision health. Apnea is one of the leading causes of death not only in the USA but also in numerous developing countries in the world, and the condition is critical among adults aged over 40. Currently, the diagnosis of apnea requires the patients to go through overnight sleep studies, such as polysomnography (PSG) or pneumocardiogram, for over 12 to 24 hours period, which is expensive and time-consuming. During sleep studies, numerous sensors are attached to the subjects’ body and face, causing them a great deal of inconvenience. The manual scoring of the apneic events from the PSG data is very time-consuming and demands specially trained sleep experts. Thus, it necessitates the importance of the design and development of a compact and wearable apnea detection device that alleviates these problems by also utilizing AI/machine learning algorithms that can automatically detect apneic events from the data collected by the sensors.
