Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging
| العنوان: | Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging |
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| المؤلفون: | Jie Zhao, Duyan Geng, Jiaji Dong, Xing Jiang |
| المصدر: | Technology and Health Care |
| بيانات النشر: | IOS Press, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Support Vector Machine, Computer science, Biomedical Engineering, Biophysics, Health Informatics, Bioengineering, Biomaterials, Electrocardiography, Heart Rate, Genetic algorithm, genetic algorithm, Heart rate variability, Humans, Computer Simulation, Time domain, Fitness function, particle swarm optimization, business.industry, Particle swarm optimization, sleep stage, Pattern recognition, Support vector machine, Frequency domain, Sleep (system call), Artificial intelligence, Sleep Stages, business, Algorithms, Biomarkers, Information Systems, Research Article |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 1878-7401 0928-7329 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::51e9f3441e0f3799e2871bd112b57726 http://europepmc.org/articles/PMC6597982 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....51e9f3441e0f3799e2871bd112b57726 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18787401 09287329 |
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