Grasshopper Optimization for R-R Interval Selection and CBNN as Classifier

Grasshopper Optimization for R-R Interval Selection and CBNN as Classifier

© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Parminder Kaur, Hardeep Singh Saini, Bikrampal Kaur
DOI : 10.14445/22315381/IJETT-V70I8P247

How to Cite?

Parminder Kaur, Hardeep Singh Saini, Bikrampal Kaur, "Grasshopper Optimization for R-R Interval Selection and CBNN as Classifier," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 463-474, 2022. Crossref,

Electrocardiogram has always been an area of motivation for researchers due to their significance in heart disease identification and classification. As different types of heart diseases result in spike changes in the ECG signal, R-R interval selection becomes crucial if the prediction must be done. This research article proposes a Swarm Intelligence-based Improved Grasshopper algorithm as RR-GHO. The grasshopper food selection policy introduces a novel grouping behaviour, and both the exploration and exploitation phases have been designed and implemented. In addition, a novel fitness function has been designed to improve the overall co-relation between the R-R intervals based on the intervals available in other groups. The optimized set has been trained using a Conjugate Based Neural Network, and the validation ratio has been kept at 70-30. The simulation has been done using MATLAB on an open-source MIT-BIH Arrhythmia dataset. The proposed algorithm architecture has also been compared with other research works in the same context based on the quantitative parameters, namely Precision, Recall, F-measure, and Accuracy. The accuracy of the proposed algorithm was improved by 11% compared to existing techniques.

ECG Monitoring, Grasshopper Optimization, Neural Network, Swarm Intelligence.

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