  The transformer based model (e.g., FusingTF) has been employed recently for
Electrocardiogram (ECG) signal classification. However, the high-dimensional
embedding obtained via 1-D convolution and positional encoding can lead to the
loss of the signal's own temporal information and a large amount of training
parameters. In this paper, we propose a new method for ECG classification,
called low-dimensional denoising embedding transformer (LDTF), which contains
two components, i.e., low-dimensional denoising embedding (LDE) and transformer
learning. In the LDE component, a low-dimensional representation of the signal
is obtained in the time-frequency domain while preserving its own temporal
information. And with the low dimensional embedding, the transformer learning
is then used to obtain a deeper and narrower structure with fewer training
parameters than that of the FusingTF. Experiments conducted on the MIT-BIH
dataset demonstrates the effectiveness and the superior performance of our
proposed method, as compared with state-of-the-art methods.
