Personalized recognition of wake/sleep state based on the combined shapelets and K-means algorithm. (January 2022)

• Record Type: Journal Article
• Authors: Geng, Duyan; Qin, Zhaoxu; Wang, Jiaxing; Gao, Zeyu; Zhao, Ning
• Is Part Of: Biomedical signal processing and control. Volume 71(2022)Part A
• Publication Date: 2022-01
• Subjects: HRV -- Shapelets -- K-Means -- Wake/sleep identification; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103132
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Unsupervised method for identifying wakefulness and sleep. Sleep/wake classification based on HRV. Shapelets combined with k-means. A new method for household sleep monitoring. Abstract: Background: Sleep affects almost all aspects, including health, memory and quality of life. It is important to distinguish wake/sleep correctly in sleep monitoring. Supervised recognition algorithms are trained on expensive and laborious polysomnography (PSG), and individual differences can't be ignored. Most of the unsupervised recognition algorithms are based on acceleration signal, and body movement seriously affects its accuracy. Objective: In order to improve the generalization ability of wake/sleep recognition and avoid the adverse effects of body movement, we propose an unsupervised method that only needs heart rate variability (HRV) to recognize wake/sleep states, and verify the classification results in a public database. Methods: Shapelets algorithm is used to quantify the similarity between HRV segments, and K-means clustering algorithm is used to improve the shapelets algorithm to realize the classification of wake/sleep. The sleep tags in the database are used to verify and compare the classification results before and after the improvement. Besides, the influence of unbalanced samples on the classification performance of the two algorithms is analyzed. Results: The accuracy of the combined shapelets and K-means (SLKM) algorithm is 0.7800 ± 0.0692, 0.8826 ± 0.0533 in two databases. Compared with the shapelets algorithm, the accuracy is improved by 11.47% and 15.49% respectively. Conclusions: This method can effectively realize wake/sleep recognition based on individual HRV. It has stronger robustness, which is very suitable for large-scale and long-time sleep monitoring.