Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
This work addresses the time-consuming and expert-dependent process of manual ECG evaluation for arrhythmia detection, offering an incremental improvement through automated architecture search.
The authors tackled the problem of automating CNN architecture design for ECG-based arrhythmia detection by proposing Heart-Darts, which uses differentiable architecture search to customize a CNN model, achieving improved performance and generalization over state-of-the-art methods on the MIT-BIH database and two others.
Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. However, manually evaluating ECG signals is a complicated and time-consuming task. With the application of convolutional neural networks (CNNs), the evaluation process has been accelerated and the performance is improved. It is noteworthy that the performance of CNNs heavily depends on their architecture design, which is a complex process grounded on expert experience and trial-and-error. In this paper, we propose a novel approach, Heart-Darts, to efficiently classify the ECG signals by automatically designing the CNN model with the differentiable architecture search (i.e., Darts, a cell-based neural architecture search method). Specifically, we initially search a cell architecture by Darts and then customize a novel CNN model for ECG classification based on the obtained cells. To investigate the efficiency of the proposed method, we evaluate the constructed model on the MIT-BIH arrhythmia database. Additionally, the extensibility of the proposed CNN model is validated on two other new databases. Extensive experimental results demonstrate that the proposed method outperforms several state-of-the-art CNN models in ECG classification in terms of both performance and generalization capability.