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.