Abstract
Electrocardiogram (ECG) indicates the state of the cardiac heart and hence acts as a yardstick to a person’s health. The anomalies may not be periodic and may appear at varied deviations and intervals since the ECG is a non-stationary, continuous and suddenly changing signal. The ECG signal must be accurately examined in order to make sensible healthcare decisions. Clinical observation of ECG can be tedious and sometimes misleading. Moreover, visual analysis cannot be relied upon. Thus, our basic objective is to minimize the errors in ECG signals and achieve more accuracy. This paper deals with the removal of root mean squared error (RMSE) estimator metrics for ECG signals with the implementation of adaptive clustering and performed the computations by using fuzzy inference system (FIS). Overall, the error rate generated by concept drift, which is signal divergence from one state to another, is inversely related to the accuracy of the classifier system. Hence, we aimed to reduce the RMSE evolved in the ECG signal and increase the accuracy. The result shows that the fuzzy inference evaluation technique is more accurate for clustering and removal of errors in terms of RMSE metric calculations for ECG signal.
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Karnewar, J.S., Shandilya, V.K. (2022). Analysis of Electrocardiogram Signal Using Fuzzy Inference Evaluation System. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_34
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DOI: https://doi.org/10.1007/978-981-16-9650-3_34
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