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APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS
Abstract
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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Authors (6)
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Amine B. Slama
- University of Tunis ENSIT, LR13ES03 SIME
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Aymen Mouelhi
- University of Tunis ENSIT, LR13ES03 SIME
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Mohamed F. Diouani
- Pasteur Institute Laboratory of Epidemiology and Veterinary Microbiology
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Mounir Sayadi
- University of Tunis ENSIT, LR13ES03 SIME
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.24425/118163
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Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Metrology and Measurement Systems
no. 25,
edition 1,
pages 87 - 101,
ISSN: 0860-8229 - Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- 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:
- Digital Object Identifier (open in new tab) 10.24425/118163
- Verified by:
- Gdańsk University of Technology
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