  Electrocardiogram (ECG) signals play critical roles in the clinical screening
and diagnosis of many types of cardiovascular diseases. Despite deep neural
networks that have been greatly facilitated computer-aided diagnosis (CAD) in
many clinical tasks, the variability and complexity of ECG in the clinic still
pose significant challenges in both diagnostic performance and clinical
applications. In this paper, we develop a robust and scalable framework for the
clinical recognition of ECG. Considering the fact that hospitals generally
record ECG signals in the form of graphic waves of 2-D images, we first extract
the graphic waves of 12-lead images into numerical 1-D ECG signals by a
proposed bi-directional connectivity method. Subsequently, a novel deep neural
network, namely CRT-Net, is designed for the fine-grained and comprehensive
representation and recognition of 1-D ECG signals. The CRT-Net can well explore
waveform features, morphological characteristics and time domain features of
ECG by embedding convolution neural network(CNN), recurrent neural
network(RNN), and transformer module in a scalable deep model, which is
especially suitable in clinical scenarios with different lengths of ECG signals
captured from different devices. The proposed framework is first evaluated on
two widely investigated public repositories, demonstrating the superior
performance of ECG recognition in comparison with state-of-the-art. Moreover,
we validate the effectiveness of our proposed bi-directional connectivity and
CRT-Net on clinical ECG images collected from the local hospital, including 258
patients with chronic kidney disease (CKD), 351 patients with Type-2 Diabetes
(T2DM), and around 300 patients in the control group. In the experiments, our
methods can achieve excellent performance in the recognition of these two types
of disease.
