C. Gurudas Nayak, G. Seshikala, Usha Desai, Sagar G. Nayak
Analysis of Variance (ANOVA), Discrete Wavelet Transform, Electrocardiogram, Principal Component Analysis, Support Vector Machine
The current paper, describes a machine learning-based approach for automatic detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features. Further, methodology comprises dimensionality reduction using Principal Component Analysis (PCA), 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 99.30%, 95.30%, 88.77%, 55.09% and 95.33%, respectively and an overall average accuracy of 97.48%, using SVM quadratic kernel. The developed methodology is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.
Cite this paper
C. Gurudas Nayak, G. Seshikala, Usha Desai, Sagar G. Nayak. (2016) Identification of Arrhythmia Classes Using Machine-Learning Techniques. International Journal of Biology and Biomedicine, 1, 48-53