Sleep Stage Classification using Laplacian Score Feature Selection Method by Single Channel EEG
Publish place: majlesi Journal of Electrical Engineering، Vol: 14، Issue: 4
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 232
This Paper With 9 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_MJEE-14-4_002
تاریخ نمایه سازی: 25 بهمن 1401
Abstract:
Sleep is a normal state in humans and the subconscious level of brain activity increases during sleep. The brain plays a prominent role during sleep, so a variety of mental and brain-related diseases can be identified through sleep analysis. A complete sleep period according to the two world standards R&K and AASM consists of seven and five steps, respectively. To diagnose diseases through sleep, it is necessary to identify different stages of sleep because the disorder at each stage indicates a certain disease. On the other hand, efficient and useful features should be selected to increase the accuracy of sleep stage classification. In this paper, at first, different statistical, entropy, and chaotic features are extracted from sleep data. Afterwards, by introducing and using the Laplacian score selector, the best feature set is selected. At the end, some conventional classification algorithms such as SVM, ANN and KNN are used to classify different sleep stages. Simulation results confirms the superiority of the proposed method based on the classification results. With the proposed algorithm, ۲, ۳, ۴, ۵ and ۶ stages of sleep were classified by SVM and decision tree with ۹۸.۰%, ۹۸.۰%, ۹۷.۳%, ۹۶.۶%, and ۹۵.۰% accuracy, which are more superior to previous method’s results.
Keywords:
Authors
Mahtab Vaezi
Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran.
Mehdi Nasri
Department of Biomedical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran.
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :