Application of Statistical Features and Multilayer Neural Network to Automatic Diagnosis of Arrhythmia by ECG Signals

• Autorzy: Slama A. B. , Lentka Ł. , Mouelhi A. , Diouani M. F. , Sayadi M. , Smulko J.

Identyfikatory

DOI

10.24425/118163

• Języki publikacji: EN

Abstrakty

EN

Abnormal electrical activity of heart can produce a cardiac arrhythmia. The electrocardiogram (ECG) is a non-invasive technique which is used as a diagnostic tool for cardiac diseases. Non-stationarity and irregularity of heartbeat signal imposes many difficulties to clinicians (e.g., in the case of myocardial infarction arrhythmia). Fortunately, signal processing algorithms can expose hidden information within ECG signal contaminated by additive noise components. This paper explores a method of de-noising ECG signal by the discrete wavelet transform (DWT) and further detecting arrhythmia by estimated statistical parameters. Parameters of the de-noised ECG signals were used to form an input data vector determining whether the examined patient suffers from a cardiac arrhythmia or not. Input data were transformed using selected linear methods in order to reduce dimension of the input vector. A neural network was used to detect illness. Compared with the results of recent studies, the proposed method provides more accurate diagnosis based on the examined ECG signal data.

Słowa kluczowe

EN

Multilayer Neural Network; arrhythmia diagnosis; ECG signal processing; principal component analysis; Fisher’s Linear Discriminant

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2018
• Tom: Vol. 25, nr 1
• Strony: 87--101
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr., wzory

Twórcy

autor

Slama A. B.

• University of Tunis, ENSIT, LR13ES03 SIME, 1008, Montfleury, Tunisia
• University Tunis ElManar, ISTMT, LR13ES07, LRBTM, Tunis, Tunisia

autor

Lentka Ł.

• Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, G. Narutowicza 11/12, 80-233 Gdańsk, Poland

autor

Mouelhi A.

• University of Tunis, ENSIT, LR13ES03 SIME, 1008, Montfleury, Tunisia

autor

Diouani M. F.

• Laboratory of Epidemiology and Veterinary Microbiology, Pasteur Institute, Tunis, Tunisia

autor

Sayadi M.

• University of Tunis, ENSIT, LR13ES03 SIME, 1008, Montfleury, Tunisia

autor

Smulko J.

• Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, G. Narutowicza 11/12, 80-233 Gdańsk, Poland

Bibliografia

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Uwagi

PL

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