Approach based on wavelet packet transform and 1D-RMLBP for drowsiness detection using EEG
Yükleniyor...
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley-Blackwell
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Early drowsiness detection may be crucial for the vehicle alertness system. Towards this, wearable technology, camera-based biophysical signals like electroencephalogram (EEG) approaches are utilised. In this Letter, the EEG-based approach is proposed to detect drowsiness. The proposed method consists of random sampling-based artificial signal augmentation, wavelet packet transform decomposition, logarithmic energy entropy, and one-dimensional region mean local binary pattern (1d-RMLBP) based feature extraction and classifier. k-Nearest neighbour and support vector machine classifiers are employed to detect the drowsiness. The MIT/BIH polysomnographic dataset has been used to test the proposed model. The proposed method has superior performance than the other methods using the same data set. The experimental results demonstrate that the proposed model could efficiently detect drowsiness from polysomnographic EEG signals.
Açıklama
Anahtar Kelimeler
Wavelet transforms, Electroencephalography, Feature extraction, Support vector machines, Entropy, Medical signal processing, Signal classification, Medical signal detection, Nearest neighbour methods, Drowsiness detection, Vehicle alertness system, Wearable technology, Camera-based biophysical signals, Random sampling-based artificial signal augmentation, Logarithmic energy entropy, Support vector machine classifiers, Polysomnographic EEG signals, 1D-RMLBP, Wavelet packet transform decomposition, One-dimensional region mean local binary pattern based feature extraction, MIT-BIH polysomnographic dataset, k-nearest neighbour classifier, Extreme learning machine, WPD
Kaynak
Electronics Letters
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
56
Sayı
25
Künye
Alçin, O. F. (December 01, 2020). Approach based on wavelet packet transform and 1D‐RMLBP for drowsiness detection using EEG. Electronics Letters, 56, 25, 1378-1381.
