Detection of sleep apnea events using electroencephalogram signals
Yükleniyor...
Dosyalar
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER SCI LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Sleep apnea is breathing disorder that leads to other disorders related to the brain and heart. This paper proposes detection of sleep apnea using a single feature Lampel-Ziv complexity of electroencephalogram (EEG) signals. Firstly, tunable-Q wavelet transform (TQWT) analyzes EEG signal into sub-bands (SBs). The Lampel-Ziv complexity (LZC) feature is computed from each SB for the discrimination of sleep apnea and control events. The Kruskal-Wallis (KW) test is applied to assess the discriminative performance of LZC feature. The statistically significant LZC feature is applied to discriminant analysis, decision tree, and ensemble classifiers for the detection of apnea events. The ensemble classification technique subspace-K-nearest neighbor provided the best classification accuracy of 96% for apnea events identification. The other classification performance measures sensitivity, specificity, F1-score, and Matthew's correlation coefficient are also attained higher values for the proposed method.
Açıklama
Anahtar Kelimeler
Sleep Apnea, Electroencephalogram (EEG) Signal, Tunable-Q Wavelet Transform, Lampel-Ziv Complexity, Ensemble Classifiers
Kaynak
Applied Acoustics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
181
Sayı
Künye
Taran, S., Bajaj, V., Sinha, G. R., & Polat, K. (2021). Detection of sleep apnea events using electroencephalogram signals. Applied Acoustics, 181, 108137.
