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Physiological signal-based biometrics are gaining increasing attention in the context of increasing privacy and security requirements. This paper proposes a novel electrocardiogram (ECG)-based algorithm to be used for human identification by integrating multiple local feature vectors with sparse-constraint-based sparse coding (SCSC) and bidirection...
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The electrocardiogram (ECG) has been proved to be the most common and effective method of studying cardiovascular disease because it is simple, non-invasive, and inexpensive. However, the differences between ECG signals are difficult to distinguish. In this paper, a model combining convolutional neural networks (CNN) with self-discipline learning (SDL) is proposed to realize the classification and identification of cardiac arrhythmia data. Comparison with a variety of deep learning frameworks based on the MIT-BIH arrhythmia dataset shows that, this model achieves a higher level of accuracy with less structure.KeywordsElectrocardiogramConvolutional neural networksSelf-discipline learning
Portable electrocardiogram (ECG) devices are general tools for diagnosing and analyzing cardiovascular diseases. However, they are limited in computation and storage resources, and it is necessary to compress the model. For mismatched data dimension between the 12-leads ECG data and the single-lead portable devices, conventional compression techniques cannot be applied to ECG classification model directly. To solve this problem, a novel adaptive knowledge-distillation-based model compression method is proposed. First, two kinds of teacher models are trained, which applies single lead and 12 leads ECG data respectively. Then, a feature extension module is built. It compensates single lead ECG data into 12 leads ECG data through generative adversarial networks (GANs). Finally, a model distillation is performed via all teacher models. In this way, the proposed approach brings a deeper level of interaction between the single lead data and 12 leads data. Experiment results show it outperforms existing diagnostic methods on our collected dataset. The F1 metric increases from 49.54% to 79.3%, which demonstrates the effectiveness of our approach.KeywordsECG classificationGenerative adversarial networkKnowledge distillation
