Comput Methods Programs Biomed - Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier.

Tópicos

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Resumo

In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were deployed for the analysis of the EEG signal: Choi-Williams distribution (CWD), continuous wavelet transform (CWT), and Hilbert-Huang Transform (HHT). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time-frequency representation of the EEG signal using Renyi's entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time-frequency distributions: CWD, CWT, and HHT. The CWT time-frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.

Resumo Limpo

work effici autom new approach sleep stage identif base new standard american academi sleep medicin aasm present propos approach employ timefrequ analysi entropi measur featur extract singl electroencephalograph eeg channel three timefrequ techniqu deploy analysi eeg signal choiwilliam distribut cwd continu wavelet transform cwt hilberthuang transform hht polysomnograph record sixteen subject use studi featur extract timefrequ represent eeg signal use renyi entropi classif extract featur done use random forest classifi perform new approach test evalu accuraci kappa coeffici three timefrequ distribut cwd cwt hht cwt timefrequ distribut outperform two distribut show excel perform accuraci kappa coeffici

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