Empirical Analysis of Apnea Syndrome Using an Artificial Intelligence-Based Granger Panel Model Approach

Abstract

Sleep apnea is a serious sleep disorder that occurs when a person's breathing is interrupted during sleep. People with untreated sleep apnea stop breathing repeatedly during their sleep. This study provides an empirical analysis of apnea syndrome using the AI-based Granger panel model approach. Data were collected from the MIT-BIH polysomnographic database (SLPDB). The panel is composed of eighteen patients, while the implementation was done using MATLAB software. The results show that, for the eighteen patients with sleep apnea, there was a significant relationship between ECG-blood pressure (BP), ECG-EEG, and EEG-blood pressure (BP). The study concludes that the long-term interaction between physiological signals can help the physician to understand the risks associated with these interactions. The study would assist physicians to understand the mechanisms underlying obstructive sleep apnea early and also to select the right treatment for the patients by leveraging the potential of artificial intelligence. The researchers were motivated by the need to reduce the morbidity and mortality arising from sleep apnea using AI-enabled technology.