DECISION SUPPORT SYSTEM FOR ARRHYTHMIA BEATS USING ECG SIGNALS WITH DCT, DWT AND EMD METHODS: A COMPARATIVE STUDY

Author:

DESAI USHA12,MARTIS ROSHAN JOY3,GURUDAS NAYAK C.4,SESHIKALA G.2,SARIKA K.1,SHETTY K. RANJAN5

Affiliation:

1. Department of Electronics and Communication Engineering, NMAM Institute of Technology, Nitte, Udupi, Karnataka 574110, India

2. School of Electronics and Communication Engineering, REVA University, Bengaluru 560064, India

3. Department of Electronics and Communication Engineering, St. Joseph Engineering College, Mangaloru 575028, India

4. Department of Instrumentation and Control Engineering, MIT, Manipal University, Manipal 576104, India

5. Department of Cardiology, Kasturba Medical College, Manipal University, Manipal 576104, India

Abstract

Electrocardiogram (ECG) signal is a non-invasive method, used to diagnose the patients with cardiac abnormalities. The subjective evaluation of interval and amplitude of ECG by physician can be tedious, time consuming, and susceptible to observer bias. ECG signals are generated due to the excitation of many cardiac myocytes and hence resultant signals are non-linear in nature. These subtle changes can be well represented and discriminated in transform and non-linear domains. In this paper, performance of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) methods are compared for automated diagnosis of five classes namely Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) beats. Six different approaches: (i) Principal Components (PCs) on DCT, (ii) Independent Components (ICs) on DCT, (iii) PCs on DWT, (iv) ICs on DWT, (v) PCs on EMD and (vi) ICs on EMD are employed in this work. Clinically significant features are selected using ANOVA test ([Formula: see text]) and fed to k-Nearest Neighbor (k-NN) classifier. We have obtained a classification accuracy of 99.77% using ICs on DWT method. Consistency of performance is evaluated using Cohen’s kappa statistic. Developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare.

Publisher

World Scientific Pub Co Pte Lt

Subject

Biomedical Engineering

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