Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging

BACKGROUND: Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure. OBJECTIVE: In order to study the effect of support vector machine (SVM) on the results of automatic sleep staging and improve the effectiveness of heart rate variability (HRV) as a sleep structure biomarker, thereby realize long term and non-contact monitoring of sleep quality. METHODS: Two kinds of parameter optimization methods are applied to stage sleep experiments when the known SVM can be used for automatic sleep staging. By factor analysis of the time domain, frequency domain, and nonlinear dynamic characteristics of subjects’ HRV signals, the accuracy of the cross-validation method (K-CV) is used as the fitness function value in genetic algorithm (GA) and particle swarm optimization (PSO). Furthermore, GA and PSO are used to optimize the SVM parameters. RESULTS: The results show that the accuracy rate of sleep stage is 64.44% when parameters are not optimized, the accuracy rate based on PSO is improved to 78.89% and the accuracy rate based on GA is improved to 84.44%. CONCLUSION: Both optimization algorithms can improve the accuracy of SVM for sleep staging and better results based on GA in the experiment.

[1]  Zbigniew R Struzik,et al.  Sleep-stage dynamics in patients with chronic fatigue syndrome with or without fibromyalgia. , 2011, Sleep.

[2]  Xiaorong Gao,et al.  [The sleep staging based on HRV analysis]. , 2006, Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi.

[3]  Licheng Jiao,et al.  Fast Sparse Approximation for Least Squares Support Vector Machine , 2007, IEEE Transactions on Neural Networks.

[4]  H. Otzenberger,et al.  Temporal relationship between dynamic heart rate variability and electroencephalographic activity during sleep in man , 1997, Neuroscience Letters.

[5]  C. Simon,et al.  Inverse coupling between ultradian oscillations in delta wave activity and heart rate variability during sleep , 2001, Clinical Neurophysiology.

[6]  R. Chervin,et al.  Sleep stage dynamics in fibromyalgia patients and controls. , 2008, Sleep medicine.

[7]  Hans L. Cycon,et al.  Sleep Stage Classification using Wavelet Transform and Neural Network , 1999 .

[8]  L. Stegagno,et al.  Heart rate variability during sleep as a function of the sleep cycle , 2003, Biological Psychology.

[9]  Tzung-Pei Hong,et al.  A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case , 2014, TheScientificWorldJournal.

[10]  Wei Sun,et al.  [Study on Sleep Staging Methods Based on Heart Rate Variability Analysis]. , 2016, Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi.

[11]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[12]  Yanchun Liang,et al.  An improved genetic algorithm with variable population-size and a PSO-GA based hybrid evolutionary algorithm , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[13]  H. P. Huang,et al.  Fuzzy Support Vector Machines for Pattern Recognition and Data Mining , 2002 .

[14]  George W. Irwin,et al.  A Novel Sparse Least Squares Support Vector Machines , 2013 .

[15]  Dorothy Bruck,et al.  Sleep abnormalities in chronic fatigue syndrome/myalgic encephalomyelitis: a review. , 2012, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.