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INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

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IJPR included in UGC-Approved List of Journals - Ref. No. is SL. No. 4812 & J. No. 63703

Published by : Advanced Scientific Research
ISSN
0975-2366
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IJPR 9[3] July - September 2017 Special Issue

July - September 9[3] 2017

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A Deep Learning Approach for Detection and Classification of QRS Contours using Single-lead ECG

Author: A. ANUHYA, VENKATA RATNAM KOLLURU, RAJESH KUMAR PATJOSHI
Abstract: Early detection of cardiovascular diseases can prevent millions of deaths every year worldwide. The aim of this research is to present a novel approach for the automatic diagnosis of Electrocardiogram (ECG) abnormalities based on detection of QRS complexes. This paper proposes an efficient Global QRS-Deep Neural Network (GQRS-DNN) model for ECG beat classification. Pre-processing and feature extraction is done with the use of traditional GQRS algorithm to extract the QRS complexes. The performance of algorithm is tested on MIT-BIH Arrhythmia database and have obtained an average Sensitivity of 99.52%, Positive Predictivity of 99.80%, F1-Score of 0.977 and Accuracy of 99.26%. The extracted features are given as inputs to the classifier to boost the performance of the system. Further, a comparison is made between the proposed DNN with SVM (Support Vector Machine) and KNN (K-Nearest Neighbors) and observed that proposed DNN method achieved the highest accuracy of 99.7%. It is noticed that the proposed network results in improved classification performance with less number of nodes and is applicable in real time for the development of smart health.
Keyword: Deep Neural Network, ECG, K-Nearest Neighbours, QRS Complex, Support Vector Machine
DOI: https://doi.org/10.31838/ijpr/2020.12.02.0001
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