Wakefulness evaluation during sleep for healthy subjects and OSA patients using a patch-type device
| Autor: | Su Hwan Hwang, Yu Jin Lee, Sang Ho Choi, Heenam Yoon, Jae-Won Choi, Kwang Suk Park, Do Un Jeong |
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| Rok vydání: | 2017 |
| Předmět: |
Adult
Male medicine.medical_specialty Movement 0206 medical engineering Health Informatics 02 engineering and technology Polysomnography Audiology 03 medical and health sciences Electrocardiography 0302 clinical medicine Heart Rate Respiratory disturbance index medicine Humans Wakefulness Monitoring Physiologic Sleep disorder Sleep Apnea Obstructive medicine.diagnostic_test business.industry Respiration Middle Aged medicine.disease 020601 biomedical engineering Computer Science Applications Obstructive sleep apnea Case-Control Studies Female Sleep onset latency Sleep (system call) Sleep Stages Sleep onset business 030217 neurology & neurosurgery Software Algorithms |
| Zdroj: | Computer methods and programs in biomedicine. 155 |
| ISSN: | 1872-7565 |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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