Effect of PCA Feature Reduction on Ventricular Ectopic Beat Classification
Avvaru Srinivasulu1, Y Dileep Kumar2
1Avvaru Srinivasulu, Department of Electronics and Instrumentation Engineering, GITAM, Bangalore (Karnataka), India.
2Y Dileep Kumar, Bio-Signal Research Center, Department of Electronics and Instrumentation Engineering, Sree Vidya Nikethan Engineering College, Tirupati  (Andhra Pradesh), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 29-32 | Volume-9 Issue-1S5 December 2019 | Retrieval Number: A10221291S52019/19©BEIESP | DOI: 10.35940/ijeat.A1022.1291S519
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Due to the cardiac diseases called Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) causes sudden cardiac death, it is crucial to recognize Ventricular Ectopic Beats (VEB) in Electrocardio- gram for the early diagnosis. There are many algorithms proposed earlier to classify the VEBs. Even though those algorithms achieved good accu- racy, the size of the feature set is large and not precise. In addition, earlier algorithms used feature reduction methods for reducing the feature set. Therefore, in this paper, we extracted only five features namely, Pre RR interval, post RR interval, QRS duration, QR slope, and RS slope. Later, we applied the Principle Component Analysis (PCA) for reducing the size of the feature set to observe the effect of Feature Reduction (FR) on the accuracy of VEB Classification. We applied different classifiers for classifying the cardiac beats in to normal and VEBs. Finally, using K-means Nearest Neighborhood (KNN) classifier and cubic Support Vec- tor Machine (SVM) classifier, we achieved 97.4% classification accuracy, 98.38%, 88.89% & 98.37% sensitivity, specificity & positive predictivity respectively. In addition, it is observed that by applying PCA-FR, the classification accuracy was reduced by a maximum of 3.7%.
Keywords: Electrocardiogram (ECG), Ventricular Ectopic beats (VEB), Feature Reduction (FR), K-means Nearest Neighborhood (KNN) Clas- Sifier, Support Vector Machine (SVM) classifier, Principle Component Analysis (PCA).
Scope of the Article: Classification