Abstract
An electrocardiogram (ECG) is a technique for capturing the electrical activity of the heart and offers a way to diagnose conditions that are related to the heart. Any irregular heartbeat that results in an anomaly in cardiac rhythm is known as an arrhythmia. Arrhythmia early identification is crucial to preventing many diseases. It is not possible to swiftly identify arrhythmias that could result in unexpected deaths by manually analyzing ECG readings. In order to build computer-aided diagnostic (CAD) systems that can automatically recognize arrhythmias, numerous studies have been published. In this paper, we offer a unique method for classifying a database of 2D ECG images using convolutional neural networks (CNN) and support vector machines (SVM) to aid in the diagnosis of arrhythmias. The MIT-BIH database served as the source for the experimented data. Results from the suggested method have an accuracy of 98.92%.
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Tuan, T.N. et al. (2023). Classifying 2D ECG Image Database Using Convolution Neural Network and Support Vector Machine. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_28
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DOI: https://doi.org/10.1007/978-3-031-35510-3_28
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