An Efficient Heartbeats Classifier Based on Optimizing Convolutional Neural Network Model
Recently, deep learning models have emerged as promising methods for the diagnosis of different diseases. Cardiac disease is among the leading life-threatening diseases on a global scale. The aim of this paper is to propose an optimized Convolutional Neural Network (CNN) model for the classification...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/857cb0a9f97345f3a4a2b3f96dc2ed87 |
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| Sumario: | Recently, deep learning models have emerged as promising methods for the diagnosis of different diseases. Cardiac disease is among the leading life-threatening diseases on a global scale. The aim of this paper is to propose an optimized Convolutional Neural Network (CNN) model for the classification of electrocardiogram (ECG) heartbeat data. The proposed ECG classification approach is designed with an optimal CNN configuration to classify cardiac arrhythmias quickly and effectively. Finding an optimal configuration for the CNN hyperparameters is time-consuming and needs extensive experimentation. To overcome this challenge, we present an optimization step for the proposed CNN model using a customized genetic algorithm. It provides an automatic suggestion for the best hyperparameter settings of the proposed CNN. The challenge in utilizing the genetic algorithm is that its operators need to be customized to handle our problem domain. Our approach accepts raw ECG signals without any preprocessing steps, which has benefit in saving the computation time. Our approach also provides a resampling step to ensure generalization, to better handle imbalanced ECG classes. Experiments show promising results of our proposed approach against other approaches whose CNN hyperparameters setting depended on numerous trials, requiring extensive ECG feature extraction steps, and do not consider imbalanced classes. The performance of our proposed approach is better than other existing methods both in terms of higher classification accuracy (98.45%), and lower computational complexity. |
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