Key result
ECGNet, a customized deep learning model using AELU activation, achieved 99% accuracy in ECG classification across five classes, outperforming benchmark models like LSTM, GRU, and TabNet.
A novel deep learning model, ECGNet, demonstrated 99% accuracy in ECG classification, highlighting its potential utility in automated diagnostic workflows for heart diseases.
Needs prospective clinical validation before use; leaves open ECGNet's diagnostic impact in real-world cardiology.
The electrocardiogram (ECG) is an important tool in diagnosing heart diseases. In this study, we introduce ECGNet a customized deep learning model that utilizes advanced activation functions and modified classifiers to enhance ECG classification. ECGNet’s performance is compared with three benchmark deep learning models. Image transformation and optimization are performed using the LabelIMG tool. ECGNet unifies an embedding layer for feature extraction, followed by two dense layers for disease detection. The proposed AELU (Advanced Exponential Linear Unit) activation, with a learning rate of 0.001, addresses the vanishing gradient issues in ReLU and Sigmoid function, allowing the absolute values of the small negative gradient to be preserved, thereby introducing higher nonlinearity in the output and higher model flexibility. Additionally, the Modified SparseMax classifier is employed to enhance the classification by mapping logits to sparse probability distributions. The ECGNet model achieves 99% accuracy, outperforming the existing methods. These matrices reflected their ability to efficiently classify the five different classes. The proposed model also compared with most trending pretrained models such as LSTM, GRU and TabNet to validate the effectiveness.
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Abubakar et al. (2025) studied Heart diseases (ECG classification). ECGNet vs. Benchmark deep learning models (LSTM, GRU, TabNet) was evaluated on Classification accuracy. ECGNet, a customized deep learning model using AELU activation, achieved 99% accuracy in ECG classification across five classes, outperforming benchmark models like LSTM, GRU, and TabNet.
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