Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study

被引:166
作者
Acharya, U. Rajendra [1 ,2 ,3 ]
Fujita, Hamido [4 ]
Adam, Muhammad [1 ]
Lih, Oh Shu [1 ]
Sudarshan, Vidya K. [1 ]
Hong, Tan Jen [1 ]
Koh, Joel E. W. [1 ]
Hagiwara, Yuki [1 ]
Chua, Chua K. [1 ]
Poo, Chua Kok [1 ]
San, Tan Ru [5 ]
机构
[1] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore, Singapore
[2] SIM Univ, Sch Sci & Technol, Dept Biomed Engn, Singapore, Singapore
[3] Univ Malaya, Fac Engn, Dept Biomed Engn, Kuala Lumpur, Malaysia
[4] IPU, Fac Software & Informat Sci, Takizawa, Iwate 0200693, Japan
[5] Natl Heart Ctr, Dept Cardiol, Singapore, Singapore
关键词
Coronary artery disease; Myocardial infarction; Electrocardiogram; Discrete cosine transform; Discrete wavelet transform; Empirical mode decomposition; EMPIRICAL MODE DECOMPOSITION; K-NEAREST NEIGHBOR; DIAGNOSIS; LOCALIZATION; TRANSFORM; ALGORITHM; PCA;
D O I
10.1016/j.ins.2016.10.013
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 (Ml). 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. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:17 / 29
页数:13
相关论文
共 76 条
[1]  
Abo-Zahhad Mohammed, 2011, Discrete Wavelet Transforms - Theory and Applications, P143
[2]   Automatic identification of cardiac health using modeling techniques: A comparative study [J].
Acharya, U. Rajendra ;
Sankaranarayanan, Meena ;
Nayak, Jagadish ;
Xiang, Chen ;
Tamura, Toshiyo .
INFORMATION SCIENCES, 2008, 178 (23) :4571-4582
[3]   Application of higher-order spectra for the characterization of Coronary artery disease using electrocardiogram signals [J].
Acharya, U. Rajendra ;
Sudarshan, Vidya K. ;
Koh, Joel E. W. ;
Martis, Roshan Joy ;
Tan, Jen Hong ;
Oh, Shu Lih ;
Muhammad, Adam ;
Hagiwara, Yuki ;
Mookiah, Muthu Rama Krishanan ;
Chua, Kok Poo ;
Chua, Chua K. ;
Tan, Ru San .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2017, 31 :31-43
[4]   Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads [J].
Acharya, U. Rajendra ;
Fujita, Hamido ;
Sudarshan, Vidya K. ;
Oh, Shu Lih ;
Adam, Muhammad ;
Koh, Joel E. W. ;
Tan, Jen Hong ;
Ghista, Dhanjoo N. ;
Martis, Roshan Joy ;
Chua, Chua K. ;
Poo, Chua Kok ;
Tan, Ru San .
KNOWLEDGE-BASED SYSTEMS, 2016, 99 :146-156
[5]   Linear and nonlinear analysis of normal and CAD-affected heart rate signals [J].
Acharya, U. Rajendra ;
Faust, Oliver ;
Sree, Vinitha ;
Swapna, G. ;
Martis, Roshan Joy ;
Kadri, Nahrizul Adib ;
Suri, Jasjit S. .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2014, 113 (01) :55-68
[6]   Wavelet transforms and the ECG: a review [J].
Addison, PS .
PHYSIOLOGICAL MEASUREMENT, 2005, 26 (05) :R155-R199
[7]   DISCRETE COSINE TRANSFORM [J].
AHMED, N ;
NATARAJAN, T ;
RAO, KR .
IEEE TRANSACTIONS ON COMPUTERS, 1974, C 23 (01) :90-93
[8]   Understanding diagnostic tests 1: sensitivity, specificity and predictive values [J].
Akobeng, Anthony K. .
ACTA PAEDIATRICA, 2007, 96 (03) :338-341
[9]   Deep learning approach for active classification of electrocardiogram signals [J].
Al Rahhal, M. M. ;
Bazi, Yakoub ;
AlHichri, Haikel ;
Alajlan, Naif ;
Melgani, Farid ;
Yager, R. R. .
INFORMATION SCIENCES, 2016, 345 :340-354
[10]  
Al-Kindi S. G., 2011, IEEE 1 MIDDL E C BIO