• Pranay Narendra Targe
• Prof. S. V. Patil
• Dr. K.S. Bhagat

Electrocardiogram (ECG) plays an imperative role in heart disease diagnostics, Arrhythmia is a cardiological disorder with prevalence of the world’s population.The purpose of this research to discusses the electrocardiogram analysis because this problems concerning health issue which encourage the present research. The main objective of our research is to analyze the acquired ECG signals using signal processing tools such as SVM and KNN classifier, classify them. Total 187 ECG data subjects were analyzed. These data were grouped in two classes i.e, Normal class, and Arrhythmia class respectively. In order to achieve this we have applied a SVM and KNN classification technique. Features are extracted in two different groups. In first part various ECG features such as heart rate variability, mean RR interval, Root Mean Square Distance of Successive R-R interval, Number of R peaks in ECG that differ more than 50 millisecond, percentage NN50, Standard Deviation of R-R series, Standard Deviation of Heart Rate,sampleEntropy and Power Spectral Entropy were extracted. The second features set consist of wavelet based features consisting of power, spectrum, entropy and standard deviation for the positive alpha, positive beta, positive gamma, positive theta and positive delta wavelets separately. Then the extracted features data is analyzed and classified using KNN and SVM. The proposed algorithm is implemented and also tested in MATLAB software. The proposed system successfully classifies the Normal, Arrhythmia signal for all three datasets under consideration with the overall accuracy of 100%. The KNN classification with wavelet features gives the best performance.

ECG, Arrhythmia, SVM, KNN, Wavelet Features, Accuracy.

"Heart Disease Prediction Using ECG Features Extraction and Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.50-55, June 2019, Available :http://www.jetir.org/papers/JETIR1908313.pdf

"Heart Disease Prediction Using ECG Features Extraction and Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp50-55, June 2019, Available at : http://www.jetir.org/papers/JETIR1908313.pdf