Comparison of information on sleep apnoea contained in two symmetric EEG recordings

• Autorzy: Prucnal Monika A. , Polak Adam G.

Identyfikatory

DOI

10.24425/mms.2019.128351

• Języki publikacji: EN

Abstrakty

EN

Electroencephalogram (EEG) is one of biomedical signals measured during all-night polysomnography to diagnose sleep disorders, including sleep apnoea. Usually two central EEG channels (C3-A2 and C4-A1) are recorded, but typically only one of them are used. The purpose of this work was to compare discriminative features characterizing normal breathing, as well as obstructive and central sleep apneas derived from these central EEG channels. The same methodology of feature extraction and selection was applied separately for the both synchronous signals. The features were extracted by combined discrete wavelet and Hilbert transforms. Afterwards, the statistical indexes were calculated and the features were selected using the analysis of variance and multivariate regression. According to the obtained results, there is a partial difference in information contained in the EEG signals carried by C3-A2 and C4-A1 EEG channels, so data from the both channels should be preferably used together for automatic sleep apnoea detection and differentiation.

Słowa kluczowe

EN

sleep apnoea detection; EEG signal; discrete wavelet transform; Hilbert transforms; analysis of variance; multivariate regression analysis

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2019
• Tom: Vol. 26, nr 2
• Strony: 229--239
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr., wzory

Twórcy

autor

Prucnal Monika A.

• Wrocław University of Science and Technology, Faculty of Electronics, B. Prusa 53/55, 50-317 Wrocław, Poland

autor

Polak Adam G.

• Wrocław University of Science and Technology, Faculty of Electronics, B. Prusa 53/55, 50-317 Wrocław, Poland

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).