Length No Longer Matters: A Real Length Adaptive Arrhythmia Classification Model With Multi-Scale Convolution

Although lots of arrhythmia classification models based on deep neural networks have been proposed, most of them can only be directly applied to inputs of a fixed length, which means the raw ECG records need to be padded or truncated before being put into the model. However, this process brings two main drawbacks: truncation may lead to information loss while padding increases calculation load. Besides, different sampling rates in other datasets may add trouble to transfer learning in this case. To address these problems, we propose a length adaptive arrhythmia classification model that can take advantage of raw ECG records of variable length. To further improve the overall performance, this model is designed with multi-scale convolution networks. We then introduce the dilated convolution that enables small convolution kernels to replace bigger traditional ones so as to enlarge feature's reception fields and reduce training parameters. Finally, an attention mechanism is implemented so that the model can get efficiently trained and output enhanced results. Extensive experiments on the benchmark MIT-BIH database prove that our method is competitive with other state-of-the-arts.