Key result
The proposed H-PSO-CNN model achieved a testing accuracy of 97% and a categorical cross-entropy error of 0.084768 for the classification of five classes of cardiac arrhythmias.
Why the study?
Identification of cardiac arrhythmias relies on ECG classification, which has been approached through mathematical and computational strategies but often requires manual selection of CNN hyperparameters.
Does a hybrid PSO-CNN model accurately classify cardiac arrhythmias from ECG signals?
Does a hybrid PSO-CNN model accurately classify cardiac arrhythmias from ECG signals?
A hybrid PSO-CNN model can accurately classify five classes of cardiac arrhythmias from ECG signals while automating the selection of CNN hyperparameters.
May support automated multi-class arrhythmia detection; hypothesis-generating and requires prospective validation before clinical adoption.
An electrocardiogram (ECG) is a non-invasive study used for the diagnosis of cardiac arrhythmias (CAs). The identification of a cardiac arrhythmia depends on its classification. This classification has been approached through different strategies, both mathematical and computational. In this work, a new computational model based on the particle swarm optimization (PSO) algorithm and convolutional neural network (CNN) is proposed for the classification of five classes of CAs obtained from the MIT-BIH Arrhythmia Dataset (MITDB). The goal of the PSO is to optimize the hyperparameters that define the layered architecture of a CNN, to increase the accuracy and decrease the categorical cross-entropy error (CE). The proposed model found a satisfactory layered architecture in 17.68 hours, obtaining an accuracy of 98% and 97%, a CE of 0.044968 and 0.084768, in training and testing, respectively. These results demonstrate that the proposed model is reliable and represents an innovative approach because it allows dispensing with the manual selection of the hyperparameters of the layered architecture of a CNN.
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Santander-Baños et al. (2023) studied Cardiac Arrhythmias (n=47). H-PSO-CNN (Hybrid Particle Swarm Optimization and Convolutional Neural Network) was evaluated on Classification accuracy. The proposed H-PSO-CNN model achieved a testing accuracy of 97% and a categorical cross-entropy error of 0.084768 for the classification of five classes of cardiac arrhythmias.
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