APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS - Publikacja - MOST Wiedzy

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APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS

Abstrakt

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 irregu- larity 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 lin- ear 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.

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
Metrology and Measurement Systems nr 25, wydanie 1, strony 87 - 101,
ISSN: 0860-8229
Język:
angielski
Rok wydania:
2018
Opis bibliograficzny:
Slama, A., Lentka Ł., Mouelhi, A., Diouani, M., Sayadi, M., Smulko J.: APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS// Metrology and Measurement Systems. -Vol. 25, iss. 1 (2018), s.87-101
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.24425/118163
Weryfikacja:
Politechnika Gdańska

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