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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52597

Title: Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study
Authors: U Rajendra Acharya
Hamido Fujita
Muhammad Adam
Oh Shu Lih
Vidya K Sudarshan
Tan Jen Hong
Joel EW Koh
Yuki Hagiwara
Chua K.Chua
Chua Kok Poo
Tan Ru San
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Coronary artery disease
Myocardial infarction
Electrocardiogram
Discrete cosine transform
Discrete wavelet transform
Empirical mode decomposition
Date: 2017-01
Issue Date: 2019-11-22T03:00:06Z
Publisher: International Journal of Approximate Reasoning
Abstract: Abstract: 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.
Relation: 377 pp.17-29
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52597
Appears in Collections:[資訊工程學系] 期刊論文

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