Chin electromyography-based motor unit decomposition for alternative screening of obstructive sleep apnea events: A comprehensive analysis

Abstract

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a prevalent sleep disorder characterized by recurrent episodes of obstructed breathing due to the relaxation of muscles in the upper airway during sleep, often linked with neuromuscular and cardiovascular disorders. This study introduces a novel method using traditional machine learning classifiers and surface electromyography (SEMG) features extracted from motor units (MUs) decomposed from chin electromyography (EMG) signals to screen for OSA events in OSAHS subjects. SEMG features were extracted from individual MUs decomposed from chin EMG segments using a novel dataset. An apnea detection algorithm was designed to label these events for OSAHS subjects across sleep stages. Analysis of motor neuron firing patterns in OSAHS subjects revealed lower activation during OSA events and higher activation during non-OSA segments. Additionally, we evaluated the proposed system on a publicly available dataset, achieving a maximum accuracy of 72% for OSAHS subjects in the midlife phase age group (40–59 years) and 72.5% for subjects in the severe phase of OSAHS using Support Vector Machines (SVM). The random forest (RF) classifier demonstrated robust performance, achieving 97% accuracy, 93.2% sensitivity, 100% specificity, 100% precision, a 96.48% F1-score, and an area under the curve (AUC) of 0.996. This system facilitates early differentiation between OSA and non-OSA events, enabling timely intervention in the mild apnea phase to prevent progression to severe OSAHS. Moreover, it offers a convenient alternative to conventional polysomnography (PSG), enhancing diagnostic accessibility and clinical management.