MATEC Web Conf.
Volume 125, 201721st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|Number of page(s)||7|
|Published online||04 October 2017|
Statistical and Learning Aided Classifier for ECG Based Predictive Diagnostic Tool
1 Department of Electronics and Communication Engineering, Guwahati, Assam, India
2 Departments of Electronics and Communication Technology, Gauhati University, Guwahati, 781014, Assam, India
3 Technical University - Sofia, Sofia 1000, “Kl. Ohridski” 8, Bulgaria
Early diagnosis and classification of long term cardiac signals are crucial issues in the treatment of heart related disorders. The available number of medical professional are not sufficient to deal with the increase patients for which design of certain machine based diagnostics tools have been accepted as a viable option. Typical Electrocardiogram (ECG) machine is helpful for monitoring the heart abnormalities only for short interval of time. Therefore, it becomes necessary to design a system which captures relevant features of the ECG signal for use with certain classifiers. In our proposed system, ECG signal elements like Q, R and S peaks are detected and heart rate estimated using Linear Discriminant Analysis (LDA), Adaptive Linear Discriminant Analysis (ALDA) and Support Vector Machine (SVM). For our work we have been used MIT BIH (Standard Arrhythmia Database).
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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