slugAn Approach To Automatically Detect Cardiac Arrhythmia
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 4
Abstract
Electrocardiogram (ECG), a non-invasive technique is used as a primary diagnostic tool for cardiovascular diseases. The main objective is to make the analysis of normal and abnormal beats easy so that the patient could be diagnosed for the heart problems in less time as well more accurately so that medical practitioners have primary information about the ailment and could start a treatment early.. However, it is very difficult to identify the minute changes in ECG signals which indicate a particular type of cardiac abnormality, hence imposing the need for a computer assisted diagnosis tool. A computer based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. In this work, we propose a methodology for the automatic detection of normal and abnormal cardiac conditions using ECG signals. ECG signals from MIT BIH arrhythmia database were used for analysis and classification. The ECG signals were first denoised using wavelet based denoising technique. After the denoising, it was subjected to QRS complex detection. The QRS complex is physiologically an important peak in the ECG signal. After detection of QRS complex, the ECG was segmented to obtain 200 samples segment as a beat for subsequent analysis. The segmented ECG signal was used for its dimensionality reduction using Principal component analysis(PCA).PCA implementation decreases the training error and the sum of the training and test times. In total 12 components were used for the pattern classification using feed forward neural network. The proposed system is clinically ready to deploy for mass screening programs. Overall, compared to previous techniques, this system is more suitable for diagnosis of cardiac arrhythmia with highest accuracy.
Authors and Affiliations
Priyanka Urban, E. Smily Jeya Jothi
Keywords
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- EP ID EP17971
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