Apnea and hypopnea event detection using EEG, EMG, and sleep stage labels in a cohort of patients with suspected sleep apnea

Abstract

Automating the screening, diagnosis, and monitoring of sleep apnea (SA) is potentially clinically useful. We present machine-learning models which detect SA and hypopnea events from the overnight electroencephalogram (EEG) and electromyogram (EMG), and we explain detection mechanisms. We tested four models using a novel data set comprising six-channel EEG and two-channel EMG recorded from 26 consecutive patients; recordings were expertly labeled with sleep stage and apnea/hypopnea events. For Model 1, EEG subband power and sample entropy were features used to train and test a random forest classifier. Model 2 was identical to Model 1, but we used EMG, not EEG. Model 3 was a simple decision strategy contingent upon sleep stage label. Model 4 was identical to Model 1, but we used EEG subband power, sample entropy, and sleep stage label. All models performed above chance (Matthews correlation coefficient, MCC $>$ 0): Model 4 (leave-one-patient-out cross-validated MCC = 0.314) outperformed Model 3 (0.230) which outperformed Models 2 and 1 (0.147 and 0.154, respectively). Results indicate that sleep stage label alone is sufficient to detect apnea/hypopnea events. Either EMG or EEG subband power and sample entropy can be used to detect apnea/hypopnea events, but these EEG features likely reflect contamination by EMG. Indeed, EMG power was modulated by apnea/hypopnea event beginning and end, and similar modulation appeared in EEG power. Machine-learning approaches to the detection of apnea/hypopnea events using overnight EEG must be explainable; they must account for EMG contamination and sleep stage.