SCG Backpropagation Based Prediction of Stressed EEG Spectrum

In this paper, feature vectors pertaining to the changes in spectral transients of sleep EEG under hot environment has been studied using wavelet transforms and feed forward neural network is employed to detect the stressed patterns. Four continuous hours of sleep EEG recordings of subjects under th...

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Veröffentlicht in:2020 Advances in Science and Engineering Technology International Conferences (ASET) S. 1 - 5
Hauptverfasser: Upadhyay, Prabhat Kumar, Nagpal, Chetna
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.02.2020
Schlagworte:
Backpropagation
EEG
Electroencephalography
EMG
EOG
Feature extraction
Heating systems
Prediction algorithms
Scaled Conjugate Gradient Algorithm (SCG)
Signal processing algorithms
Sleep
Stress Classification
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Zusammenfassung:In this paper, feature vectors pertaining to the changes in spectral transients of sleep EEG under hot environment has been studied using wavelet transforms and feed forward neural network is employed to detect the stressed patterns. Four continuous hours of sleep EEG recordings of subjects under the exposure of high temperature and also at room temperature have been filtered and visually analyzed. Three sleep stages: AWAKE, SWS (Slow Wave Sleep) and REM (Rapid Eye Movement) along with EMG and EOG activities under heat stress and without heat stress were quantified in time-frequency domain. Features extracted in terms of wavelet coefficients are further classified using scaled conjugate gradient algorithm (SCG). Classification accuracy of SCG algorithm is found to be 97.12% and 95.3% for stress and control subjects respectively, which may be considered as an efficient prediction model.
DOI:10.1109/ASET48392.2020.9118324