Wearable devices have dramatically developed over the past decade as their functions extended from the simple posture analysis to non-invasive condition monitoring for early warning and proactive healthcare, which are especially significant for the dangerous disease such as cardiac arrhythmia. However, it is difficult for the wearable devices to collect plentiful and high-quality training samples so as to meet the fundamental requirements for the learning-based methods. To address this challenge, we propose a meta-transfer based few-shot learning method to handle arrhythmia classification with the ECG signal from the wearable devices. First, the original ECG signals are converted into spectrograms applicable to the 2D-CNN models. Second, we propose the special large-training scheme to pre-train the feature extractor to emphasize the meaningful information for classification, and the feature output dimension is reshaped to reduce the influence of irrelevant and redundant information. Then, the meta-transfer scheme is developed to avoid the training from scratch, which is prone to overfitting without the adequate samples. Finally, we conduct the extensive experiments to assess the performance of our method. The experimental results illustrate that the proposed method outperforms in accuracy than other comparative methods when handling the various few-shot tasks under the same training samples.

在过去的十年中，可穿戴设备的功能得到了飞速发展，其功能从简单的姿势分析扩展到了用于早期预警和主动医疗的无创状态监测，这对于诸如心律不齐等危险疾病尤其重要。但是，可穿戴设备很难收集大量高质量的训练样本来满足基于学习方法的基本要求。为了解决这一挑战，我们提出了一种基于元传输的一次性学习方法，以可穿戴设备的ECG信号处理心律失常分类。首先，将原始ECG信号转换为适用于2D-CNN模型的频谱图。第二，我们提出了一种特殊的大型训练方案，对特征提取器进行预训练，以强调有意义的分类信息，并对特征输出维进行重塑，以减少无关和冗余信息的影响。然后，开发了元传输方案来避免从头开始的训练，这种训练容易在没有足够样本的情况下过度拟合。最后，我们进行了广泛的实验，以评估我们方法的性能。实验结果表明，在相同训练样本下处理各种少拍任务时，所提方法的准确性优于其他比较方法。开发元传输方案是为了避免从头开始进行培训，因为如果没有足够的样本，则容易过度拟合。最后，我们进行了广泛的实验，以评估我们方法的性能。实验结果表明，在相同训练样本下处理各种少拍任务时，所提方法的准确性优于其他比较方法。开发元传输方案是为了避免从头开始进行培训，因为如果没有足够的样本，则容易过度拟合。最后，我们进行了广泛的实验，以评估我们方法的性能。实验结果表明，在相同训练样本下处理各种少拍任务时，所提方法的准确性优于其他比较方法。