Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals

This paper explores the use of deep learning techniques for detecting sleep apnea. Sleep apnea is a common sleep disorder characterized by abnormal breathing pauses or infrequent breathing during sleep. The current standard for diagnosing sleep apnea involves overnight polysomnography, which is expensive and requires specialized equipment and personnel. The proposed method utilizes a neural network to analyze physiological signals, such as heart rate and respiratory patterns, that are recorded during sleep to authomatic sleep apnea detection. The neural network is trained on a dataset of polysomnography recordings to identify patterns that are indicative of sleep apnea. The results compare the use of different physiological signals to detect sleep apnea. Nasal airflow seems to have the most accurate results and higher specificity, whereas EEG and ECG have higher levels of sensitivity. The best model concerning accuracy is compared to bias models previously applied to sleep apnea detection in literature, achieving greater results. This approach has the potential to provide automatic sleep apnea detection, being an accessible solution for diagnosing sleep apnea.