Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques

• Published in: 2015 Annual IEEE India Conference (INDICON) pp. 1 - 4
• Main Authors: Desai, Usha, Martis, Roshan Joy, Nayak, C. Gurudas, Sarika, K., Seshikala, G.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2015
• Subjects: Accuracy; Analysis of variance; Analysis of Variance (ANOVA); Arrhythmia; Classification; Diagnosis; Discrete Wavelet Transform; Discrete wavelet transforms; Diseases; Echocardiography; Electrocardiogram; Electrocardiography; Feature extraction; Independent Component Analysis; Kernel; Methodology; Support Vector Machine; Support vector machines
• DOI: 10.1109/INDICON.2015.7443220

Electrocardiogram (ECG) remains the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. Likewise, minute variations in time-domain features viz. amplitude, segments and intervals are difficult to interpret by naked eye. The current paper, describes a machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features. Further, methodology comprises dimensionality reduction using Independent Component Analysis (ICA), ten-fold cross-validation and classification using Support Vector Machine (SVM) kernel functions. Using ANOVA significant features are selected and reliability of accuracy is measured by Cohen's kappa statistic. Large dataset of 110,093 heartbeats from 48 records of MIT-BIH arrhythmia database recommended by ANSI/AAMI EC57:1998, which are grouped into five classes of arrhythmia beats viz. Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) are classified with class specific accuracy of 99.57%, 97.91%, 92.18%, 76.54% and 97.22% respectively and an overall average accuracy of 98.49%, using SVM quadratic kernel. The developed methodology is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.