Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search

TL;DR

This paper introduces Heart-Darts, an automated method using differentiable architecture search to design CNN models for ECG heartbeat classification, significantly improving accuracy and generalization over existing models.

Contribution

The paper presents a novel automated CNN architecture design approach for ECG classification using differentiable architecture search, reducing reliance on expert-designed models.

Findings

Outperforms state-of-the-art CNN models in accuracy

Demonstrates strong generalization across multiple ECG databases

Efficiently designs CNN architectures tailored for ECG signals

Abstract

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.