Key result
A preprocessing-free convolutional neural network model using multicycle ECG images and a focal loss function outperformed existing methods for arrhythmia classification.
Why the study?
Existing machine learning models for arrhythmia classification face challenges with input coverage from single cardiac cycles, data imbalance for rare arrhythmias, and poor generalizability to unseen data.
Does a preprocessing-free CNN model using multicycle ECG images and focal loss improve arrhythmia classification performance compared to existing methods?
Does a preprocessing-free CNN model using multicycle ECG images and focal loss improve arrhythmia classification performance compared to existing methods?
A novel preprocessing-free CNN model using multicycle ECG images and focal loss improves arrhythmia classification, particularly for rare arrhythmias and unseen data.
May aid automated arrhythmia detection on ECG; leaves open prospective validation before clinical adoption.
Arrhythmia, which is characterized by irregular heart rhythms, can lead to life-threatening conditions by disrupting the circulatory system. Thus, early arrhythmia detection is crucial for timely and appropriate patient treatment. Machine learning models have been developed to classify arrhythmia using electrocardiogram (ECG) data, which effectively capture the patterns associated with different abnormalities and achieve high classification performance. However, these models face challenges in terms of input coverage and robustness against data imbalance issues. Typically, existing methods employ a single cardiac cycle as the input, possibly overlooking the intervals between cycles, potentially resulting in the loss of critical temporal information. In addition, limited samples for rare arrhythmia types restrict the involved model’s ability to effectively learn, frequently resulting in low classification accuracy. Furthermore, the classification performance of existing methods on unseen data is not satisfactory owing to insufficient generalizability. To address these limitations, this research proposes a convolutional neural network (CNN) model for arrhythmia classification that incorporates two specialized modules. First, the proposed model utilizes images of three consecutive cardiac cycles as the input to expand the learning scope. Second, we implement a focal loss (FL) function during model training to prioritize minority classes. The experimental results demonstrate that the proposed model outperforms existing methods without requiring data preprocessing. The integration of multicycle ECG images and the FL function substantially enhances the model’s ability to capture ECG patterns, particularly for minority classes. In addition, our model exhibits satisfactory classification performance on unseen data from new patients. These findings suggest that the proposed model is a promising tool for practical application in arrhythmia classification tasks.
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Prathom et al. (2025) studied Arrhythmia. Preprocessing-Free Convolutional Neural Network (CNN) Model vs. Existing methods was evaluated on Arrhythmia classification performance. A preprocessing-free convolutional neural network model using multicycle ECG images and a focal loss function outperformed existing methods for arrhythmia classification.
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