Negative-ResNet: noisy ambulatory electrocardiogram signal classification scheme.

With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing. However, data labelling quality has become one of the most significant issues for AI applications, especially when it requires domain knowledge (e.g. medical image labelling). Besides, there might be noisy labels in practical datasets, which impairs the training process of neural networks. In this paper, we propose a semi-supervised algorithm for training data samples with noisy labels by performing selected positive learning and negative learning. To verify the effectiveness of the proposed scheme, we designed a portable ECG patch-RealCare and applied the algorithm on a real-life dataset. Our experimental results show that we can achieve an accuracy of 91.0%, which is 6.2% higher than a normal training process with ResNet. There are 65 patients in our dataset, and we randomly picked 2 patients to perform validation. [ABSTRACT FROM AUTHOR]