  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.
