TY - EJOUR
PY - 2024
DA - February
TI - Automotic Recognition of Sleep Spindles Based on Two-Stage Classifier with Artificial Neural Networks and Support Vector Machines
T2 - Majlesi Journal of Electrical Engineering
VL - 2
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/automotic-recognition-of-sleep-spindles-based-on-two-stage-classifier-with-artificial-neural-networks-and-support-vector-machines/
DO - 10.1234/mjee.v2i1.45
N2 - 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 19-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 19-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 91.4%  average sensitivity and 3.85% average false detection rate.
IS - 1
PB - OICC Press
KW - EEG, Sleep spindle recognition, Support Vector Machines, back propagation algorithm. 
EN -