Abstract:
Detection of abnormalities in the ECG signal to achieve an automatic
diagnosis of several heart related diseases has become an increased research aspect.
This paper focused to develop an automatic detection system to detect abnormalities
in ECG. These abnormalities results in different cardiac arrhythmias. Towards the
detection of different cardiac arrhythmias, this paper analyzed the ECG signal
through Dual Tree Complex Wavelet Transform (DTCWT) as a feature extraction
technique and further proposed a new selective band coding technique to extract only
the informative features from the sub bands obtained from DTCWT. The novelty of
this proposed system is to remove the redundant information, thereby achieving a fast
and accurate detection results. Multi-Class Support Vector Machine (MC-SVM) is
used for classification purpose. Extensive simulations are carried out for the MITBIH
database and the performance is measured through the performance metrics
such as Accuracy, Precision, Recall, False Positive Rate, F-Measure and overall
computational time. The proposed method is also compared with conventional
approaches to alleviate the performance enhancement in the detection of Cardiac
Arrhythmias (CAs) with less time span.
Keywords:
Accuracy,Cardiac Arrhythmia,Detection Rate,DTCWT,ECG,MCSVM,SA,
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