Estimating sleep parameters using nasal pressure signals applicable to continuous positive airway pressure devices

Abstract

Objective: This paper proposes a method for classifying sleep–wakefulness and estimating sleep parameters using nasal pressure signals applicable to a continuous positive airway pressure (CPAP) device. Approach: In order to classify the sleep–wakefulness states of patients with sleep-disordered breathing (SDB), apnea-hypopnea and snoring events are first detected. Epochs detected as SDB are classified as sleep, and time-domain- and frequency-domain-based features are extracted from the epochs that are detected as normal breathing. Subsequently, sleep–wakefulness is classified using a support vector machine (SVM) classifier in the normal breathing epoch. Finally, four sleep parameters—sleep onset, wake after sleep onset, total sleep time and sleep efficiency—are estimated based on the classified sleep–wakefulness. In order to develop and test the algorithm, 110 patients diagnosed with SDB participated in this study. Ninety of the subjects underwent full-night polysomnography (PSG) and twenty underwent split-night PSG. The subjects were divided into 50 patients of a training set (full/split: 42/8), 30 of a validation set (full/split: 24/6) and 30 of a test set (full/split: 24/6). Main results: In the experiments conducted, sleep–wakefulness classification accuracy was found to be 83.2% in the test set, compared with the PSG scoring results of clinical experts. Furthermore, all four sleep parameters showed higher correlations than the results obtained via PSG (r ⩾ 0.84, p < 0.05). In order to determine whether the proposed method is applicable to CPAP, sleep–wakefulness classification performances were evaluated for each CPAP in the split-night PSG data. The results indicate that the accuracy and sensitivity of sleep–wakefulness classification by CPAP variation shows no statistically significant difference (p < 0.05). Significance: The contributions made in this study are applicable to the automatic classification of sleep–wakefulness states in CPAP devices and evaluation of the quality of sleep.