Spectral entropy and deep convolutional neural network for ECG beat classification

• Autorzy: Asgharzadeh-Bonab Akbar , Amirani Mehdi Chehel , Mehri Alaeddin

Identyfikatory

DOI

10.1016/j.bbe.2020.02.004

• Języki publikacji: EN

Abstrakty

EN

Sudden cardiac death is the result of abnormal heart conditions. Therefore, early detection of such abnormal conditions is vital to identify heart problems. Hence, in this paper, we aim to present a new computer-aided diagnosis (CAD) method based on time-frequency analysis of electrocardiogram (ECG) signals and deep neural networks for arrhythmia detection. Time-frequency transforms have the capability of providing spectral information at different times, which is very useful for analyzing non-stationary signals. On the other side, entropy is an attractive measurement from ECG signals which can distinguish different types of them. In this paper, time-frequency spectral entropy is proposed to extract the efficient features from ECG signals. All computed entropies cannot provide separability among different classes, two-directional two-dimensional principal component analysis (2D²PCA) can be used to reduce the dimension of the extracted features. Finally, the convolutional neural network (CNN) classifies the time-frequency features to diagnose the ECG beat signals and detect arrhythmias. The results show that the spectral entropy can provide good separation between different among ECG beats and the proposed method outperforms the recently introduced method for analyzing ECG signals.

Słowa kluczowe

EN

cardiac arrhythmia; convolutional neural network; electrocardiogram; spectral entropy; principal component analysis

PL

arytmia serca; konwolucyjna sieć neuronowa; elektrokardiogram; analiza składników głównych

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 2
• Strony: 691--700
• Opis fizyczny: Bibliogr. 51 poz., rys., tab., wykr.

Twórcy

autor

Asgharzadeh-Bonab Akbar

• Department of Electrical Engineering, Urmia University, Urmia, Iran

autor

Amirani Mehdi Chehel

• Department of Electrical Engineering, Urmia University, Urmia, Iran

autor

Mehri Alaeddin

• Cardiologists, Azerbaijan Hospital, Urmia, Iran

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).

PL

W opisie bibliogr. brak pozycji nr 40.