Development of an automated system for obstructive sleep apnea treatment based on machine learning and breath effort monitoring

Abstract

Obstructive sleep apnea (OSA) is characterized by repeated airway obstructions during sleep and affects about 35% of the population. Untreated OSA is associated with increased mortality and severe comorbidities. This fact, combined with the poor long-term compliance to the standard therapy, has led to an increased interest in alternative treatment options such as the electrical stimulation (ES) of the genioglossus muscle. In this work, we propose an automated non-invasive system for real-time monitoring of obstructive sleep apnea and treatment through ES of the genioglossus muscle. The closed-loop system includes a sensor to monitor breath effort from respiratory movement, a Transcutaneous Electrical Nerve Stimulation (TENS) device, and a program that analyzes the signal of the sensor and controls the whole system. The breath effort signal is first processed and then fed to a machine learning (ML) algorithm, which is a pattern recognition network. The whole analysis runs on a personal computer and used data from an open database of 12 patients in order to train the ML network and evaluate its performance. The breath effort signals were obtained from thoracic and abdominal inductance plethysmography recordings. OSA events were classified with an average true detection rate 81% ± 8% and 77% ± 11% for thoracic (VTH) and abdominal (VAB) sensor signals, respectively. The overall classification accuracies were on average 73% ± 5% for VTH and 74% ± 9% for VAB. An improvement is observed when both signals are considered (82% ± 7%).