A New Fuzzy Based Ensemble Classifier for Analysis of ECG Signal
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 4
Abstract
ECG(electrocardiogram) reflects the state of cardiac heart and hence is like a pointer to the health conditions of a human being. However, ecg being anon-stationary, continuous in nature and abruptly changing signal, the irregularities may not be periodic and may show up at different intervals. For taking intelligent health care decisions, ecg signal needs to be analyzed accurately. Clinical observation of ecg can take long hours and can be very tedious. Moreover, visual analysis cannot be relied upon.thus, our basic objective is to come up with an ensemble based classification technique that will classify ecg signal with the more accuracy. This objective has motivated us to search and experiment with various ecg signals by categorizing it in correct class and simultaneously achieving maximum accuracy of the ensemble classifier. This paper deals with the implementation of a fuzzy based ensemble classifier that performs the computations by using fuzzy inference system (fis) to classify the ecg and to achieve the maximum accuracy. Overall, we have tried to minimize the concept drift evolved in the ecg signal andmaximize the accuracy because the error rate introduced due to concept drift is inversely proportional to the accuracy of ensemble based classifier.the result shows that the ensemble classifier with the fuzzy based technique is more accurate up to 99% in classification of ecg signal.
Authors and Affiliations
Girish B Umaratkar, Sachin A Murab
Keywords
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- EP ID EP23885
- DOI http://doi.org/10.22214/ijraset.2017.4179
- Views 273
- Downloads 9
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