Abstract
This paper presents a feedforward multilayer perceptron neural network with a Levenberg-Marquardt learning algorithm for recognizing arrhythmia disease from normal electrocardiogram (ECG) patterns. To the best of our knowledge, in the field of arrhythmia disease classification, classical approaches utilize either different QRS complex detection or feature reduction methods but not both at the same time; thus, this work provides an important contribution. A total of forty-four records were obtained from the MIT-BIH arrhythmia database to test the QRS complex detection method, and the obtained results were a specificity of 96.16% and a sensitivity of 98.03%. The best classification rate obtained using the presented approach was 98.27%.
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Acknowledgements
This project is supported by Jan Evangelista Purkyně University. Title of the project: Internet of Things-Arrhythmia Disease Monitoring and Classification.
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Rodríguez-Jorge, R., Bíla, J., Škvor, J. (2022). Neural Network with L-M Algorithm for Arrhythmia Disease Classification. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2021. Lecture Notes in Networks and Systems, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-030-89899-1_33
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DOI: https://doi.org/10.1007/978-3-030-89899-1_33
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