Smart technologies toward sleep monitoring at home
10.1007/s13534-018-0091-2
- Author:
Kwang Suk PARK
1
;
Sang Ho CHOI
Author Information
1. Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 03080, Korea. pks@bmsil.snu.ac.kr
- Publication Type:Review
- Keywords:
Sleep monitoring;
Unconstrained;
Nonintrusive;
Polysomnography;
Ubiquitous healthcare
- MeSH:
Actigraphy;
Brain;
Delivery of Health Care;
Electrocardiography;
Electroencephalography;
Mycobacterium bovis;
Polysomnography;
Respiration;
Sleep Stages
- From:
Biomedical Engineering Letters
2019;9(1):73-85
- CountryRepublic of Korea
- Language:English
-
Abstract:
With progress in sensors and communication technologies, the range of sleep monitoring is extending from professional clinics into our usual home environments. Information from conventional overnight polysomnographic recordings can be derived from much simpler devices and methods. The gold standard of sleep monitoring is laboratory polysomnography, which classifi es brain states based mainly on EEGs. Single-channel EEGs have been used for sleep stage scoring with accuracies of 84.9%. Actigraphy can estimate sleep effi ciency with an accuracy of 86.0%. Sleep scoring based on respiratory dynamics provides accuracies of 89.2% and 70.9% for identifying sleep stages and sleep effi ciency, respectively, and a correlation coeffi cient of 0.94 for apnea–hypopnea detection. Modulation of autonomic balance during the sleep stages are well recognized and widely used for simpler sleep scoring and sleep parameter estimation. This modulation can be recorded by several types of cardiovascular measurements, including ECG, PPG, BCG, and PAT, and the results showed accuracies up to 96.5% and 92.5% for sleep effi ciency and OSA severity detection, respectively. Instead of using recordings for the entire night, less than 5 min ECG recordings have used for sleep effi ciency and AHI estimation and resulted in high correlations of 0.94 and 0.99, respectively. These methods are based on their own models that relate sleep dynamics with a limited number of biological signals. Parameters representing sleep quality and disturbed breathing are estimated with high accuracies that are close to the results obtained by polysomnography. These unconstrained technologies, making sleep monitoring easier and simpler, will enhance qualities of life by expanding the range of ubiquitous healthcare.
