Why the study?
Does a Support Vector Machine and Cuckoo Search Optimized Neural Network improve the automatic detection of heart disease from ECG signals?
Does a Support Vector Machine and Cuckoo Search Optimized Neural Network improve the automatic detection of heart disease from ECG signals?
An algorithm using SVM and Cuckoo Search Optimized Neural Network achieved 99.50% overall recognition performance for detecting ECG abnormalities.
High ECG classification accuracy requires prospective clinical validation; leaves open routine use for heart disease detection.
This paper tried to address several topics concerning the analysis, synthesis and compression of the electrocardiogram signal (ECG) using the MIT database. We detect the R-wave by identifying the location of each interval delineating a QRS complex using unbiased and biased estimators. In the second part of the work, we segmented the signal into RR periods constituting the vectors of a data matrix, where we extracted its main components in order to reduce the size of the cardiac information, and then further reduced in addition the size by the use of a threshold on the signal. Then the classification is done for automatic detection of heart disease using Support Vector Machine (SVM) and Cuckoo Search Optimized Neural Network. ECG beats with 4 types of abnormalities (RBBB, APC, PVC and LBBB) from ECG records is retrieved from the MIT-BIH arrhythmia database. Analysis of the different groups shows the overall recognition performance was 99.50%. The worst is 99.63% for the RBBB class.
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Patil et al. (2018) studied this question.
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