Wakefulness evaluation during sleep for healthy subjects and OSA patients using a patch-type device. (March 2018) - Yoon, Heenam; Hwang, Su Hwan; Choi, Sang Ho; Choi, Jae-Won; Lee, Yu Jin; Jeong, Do-Un; Park, Kwang Suk
Highlights: Patch-type device used to collect ECG and 3-axis accelerometer signals. Six separate methods developed to detect different types of wakefulness. Results for wakefulness-associated information compared well with PSG results. Proposed algorithm strongly outperforms conventional classifiers. Proposed algorithm could be combined with algorithms for REM and SWS detection. Abstract: Objectives: Obstructive sleep apnea (OSA) is a major sleep disorder that causes insufficient sleep, which is linked with daytime fatigue and accidents. Long-term sleep monitoring can provide meaningful information for patients with OSA to prevent and manage their symptoms. Even though various methods have been proposed to objectively measure sleep in ambulatory environments, less reliable information was provided in comparison with standard polysomnography (PSG). Therefore, this paper proposes an algorithm for distinguishing wakefulness from sleep using a patch-type device, which is applicable for both healthy individuals and patients with OSA. Methods: Electrocardiogram (ECG) and 3-axis accelerometer signals were gathered from the single device. Wakefulness was determined with six parallel methods based on information about movement and autonomic nervous activity. The performance evaluation was conducted with five-fold cross validation using the data from 15 subjects with a low respiratory disturbance index (RDI) and 10 subjects with high RDI. In addition, wakefulness information, including total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO), were extracted from the proposed algorithm and compared with those from PSG. Results: According to epoch-by-epoch (30 s) analysis, the performance results of detecting wakefulness were an average Cohen's kappa of 0.60, accuracy of 91.24%, sensitivity of 64.12%, and specificity of 95.73%. Moreover, significant correlations were observed in TST, SE, SOL, and WASO between the proposed algorithm and PSG ( p < 0.001). Conclusions: Wakefulness-related information was successfully provided using data from the patch-type device. In addition, the performance results of the proposed algorithm for wakefulness detection were competitive with those from previous studies. Therefore, the proposed system could be an appropriate solution for long-term objective sleep monitoring in both healthy individuals and patients with OSA.