Key result
Proposed CNN models outperform state-of-the-art algorithms with ~99% accuracy on ECG data.
Why the study?
A robust algorithm was needed to accurately classify electrocardiogram signals even in the presence of environmental noise.
Does a hybrid 1D and 2D CNN model improve the classification accuracy of ECG signals for arrhythmia detection compared to state-of-the-art algorithms?
Does a hybrid 1D and 2D CNN model improve the classification accuracy of ECG signals for arrhythmia detection compared to state-of-the-art algorithms?
A proposed 2D CNN model achieved 99.02% accuracy in detecting arrhythmias from ECG signals, outperforming existing state-of-the-art algorithms.
Answers are grounded in this paper and cite their sources.
May aid noisy ECG arrhythmia detection; extends algorithmic benchmarks but remains hypothesis-generating pending clinical validation.
Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.
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Ullah et al. (2021) studied this question. The proposed 1D and 2D CNN models achieved classification accuracies of 97.38% and 99.02%, respectively, outperforming state-of-the-art algorithms on ECG data.
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