Abstract
AbstractRecent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.
Funder
Kementerian Riset, Teknologi dan Pendidikan Tinggi
Publisher
Springer Science and Business Media LLC
Subject
Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems
Reference37 articles.
1. Hirshkowitz M. National Sleep Foundation 2013 poll. In: Sleep Heal; 2013.
2. Tibbitts GM. Sleep disorders: causes, effects, and solutions. Prim Care Clin Off Pract. 2008;35:817–37. https://doi.org/10.1016/j.pop.2008.07.006.
3. Kales A, Rechtschaffen A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Neurological Information Network: U. S. National Institute of Neurological Diseases and Blindness; 1968.
4. J. Nedoma et al., “Comparison of BCG, PCG and ECG signals in application of heart rate monitoring of the human body,” 2017 40th Int. Conf. Telecommun. Signal Process. TSP 2017, vol. 2017-Janua, pp. 420–424, 2017, https://doi.org/10.1109/tsp.2017.8076019.
5. Majumder S, Chen L, Marinov O, Chen CH, Mondal T, Jamal Deen M. Noncontact Wearable Wireless ECG Systems for Long-Term Monitoring. IEEE Rev Biomed Eng. 2018;11:306–21. https://doi.org/10.1109/RBME.2018.2840336.
Cited by
29 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献