Abstract
Early detection of arrhythmia assists clinicians in timely treatment that improves the recovery rate and reduces the mortality rate. The existing research works employed various models to improve Arrhythmia detection performance. The existing models has low classification performance, due to irrelevant feature selection. In this research, K-Means Monarchy Butterfly Optimization (KMMBO) is used as a feature selection method for arrhythmia detection. The Monarchy Butterfly Optimization method escapes from local optima and provides good convergence in the search process. In this research, the role of k-means algorithm is to cluster the current generation population (feature subsets) from the Monarchy Butterfly algorithm into two groups to generate new solutions for the next generation. The relevant features selected by the KMMBO method are stored in a memory cell of Bi-directional Long Short Term Memory (Bi-LSTM) for long term that provides improvement in arrhythmia detection performance. The KMMBO method selection process avoids overfitting problems and imbalance data problems by focusing on unique features of the classes that increases the model sensitivity. The proposed KMMBO method’s efficiency is analyzed on the China Physiological Signal Challenge 2018 (CPSC 2018) and Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) datasets. Next, Dual Tree Complex Wavelet Transform (DTCWT), statistical, and entropy features are utilized for feature extraction and then KMMBO is applied for selecting optimum features. The KMMBO-BiLSTM model has better accuracy and sensitivity of 98.48% and 99.85% on the MIT-BIH dataset. In addition to this, the proposed KMMBO-BiLSTM model attained accuracy and sensitivity of 97.22% and 97.61% on the CPCS 2018 dataset.
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Data availability
The datasets generated during and/or analyzed during the current study are available in the MIT-BIH/CPSC 2018 repository, https://archive.physionet.org/cgi-bin/atm/ATM, http://2018.icbeb.org/Challenge.html.
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RM contributed to conceptualization, implementation, resource and data acquisition. NG contributed to writing, validation, and visualization.
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Mogili, R., Narsimha, G. K-means monarchy butterfly optimization for feature selection and Bi-LSTM for arrhythmia classification. Soft Comput 27, 14935–14951 (2023). https://doi.org/10.1007/s00500-023-08579-x
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DOI: https://doi.org/10.1007/s00500-023-08579-x