Automotic Recognition of Sleep Spindles Based on Two-Stage Classifier with Artificial Neural Networks and Support Vector Machines
محل انتشار: مجله مهندسی برق مجلسی، دوره: 2، شماره: 1
سال انتشار: 1387
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 44
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شناسه ملی سند علمی:
JR_MJEE-2-1_008
تاریخ نمایه سازی: 8 آبان 1402
چکیده مقاله:
Sleep spindles are one of the most important transient waveforms found in the sleep EEG signal. Here, we introduce a two-stage procedure based on artificial neural networks for the automatic recognition of sleep spindles (SS) in a ۱۹-channel electroencephalographic signal. In the first stage, a pre-processing perception is used for enhancing overall detection and also reducing computation time. In the second stage, the selected Sleep spindles (SS), classified with neural network post-classifier. Classifying tools in post-processing procedure were MLP and RBSVM that their operations are compared in the last section of the report. Visual inspection of ۱۹-channel EEG from six subjects by one expert in this theme, showed that RBSVM operation is better than MLP with BP (Back propagation) training, that SVM provided ۹۱.۴% average sensitivity and ۳.۸۵% average false detection rate.
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