Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study
| Autor: | Joel Ew Koh, Tan Ru San, Oh Shu Lih, Chua K. Chua, Chua Kok Poo, U. Rajendra Acharya, Tan Jen Hong, Hamido Fujita, Vidya K. Sudarshan, Muhammad Adam, Yuki Hagiwara |
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| Rok vydání: | 2017 |
| Předmět: |
Discrete wavelet transform
Information Systems and Management Computer science 0206 medical engineering 02 engineering and technology Hilbert–Huang transform Theoretical Computer Science Coronary artery disease Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Discrete cosine transform cardiovascular diseases Myocardial infarction business.industry Pattern recognition medicine.disease 020601 biomedical engineering Computer Science Applications Control and Systems Engineering 020201 artificial intelligence & image processing Artificial intelligence Ecg signal business Classifier (UML) Software |
| Zdroj: | Information Sciences. 377:17-29 |
| ISSN: | 0020-0255 |
| Popis: | Classification of three types of ECG beats are proposed.Normal, CAD and MI are three classes considered in this work.DCT, DWT and EMD decomposition methods are employed on ECG beat.Decomposed signals are fed to Locality Preserving Projections method.KNN classifier yielded 98.5% accuracy with seven features for DCT method. Cardiovascular diseases (CVDs) are the main cause of cardiac death worldwide. The Coronary Artery Disease (CAD) is one of the leading causes of these CVD deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of heart muscle death called Myocardial Infarction (MI). Normally, the presence of these cardiac conditions is primarily reflected on the electrocardiogram (ECG) signal. However, it is challenging and requires rich experience to manually interpret the visual subtle changes occurring in the ECG waveforms. Thus, many automated diagnostic systems are developed to overcome these limitations. In this study, the performance of an automated diagnostic system developed for detection of CAD and MI using three methods such as Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Discrete Cosine Transform (DCT) are compared. In this study, ECG signals are subjected to DCT, DWT and EMD to obtain respective coefficients. These coefficients are reduced using Locality Preserving Projection (LPP) data reduction method. Then, the LPP features are ranked using F-value. Finally, the highly ranked coefficients are fed into the K-Nearest Neighbor (KNN) classifier to achieve the best classification performance. Our proposed system yielded highest classification results of 98.5% accuracy, 99.7% sensitivity and 98.5% specificity using only seven features obtained using DCT technique. The screening system can help the cardiologists in detecting the CAD and hence presents any possible MI by prescribing suitable medications. It can be employed in routine community screening, old age homes, polyclinics and hospitals. Display Omitted |
| Databáze: | OpenAIRE |
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