This paper proposes a low-cost and highly accurate ECG-monitoring system intended for personalized early arrhythmia detection for wearable mobile sensors. Earlier supervised approaches for personalized ECG monitoring require both abnormal and normal heartbeats for the training of the dedicated classifier. However, in a real-world scenario where the personalized algorithm is embedded in a wearable device, such training data is not available for healthy people with no cardiac disorder history. In this study, (i) we propose a null space analysis on the healthy signal space obtained via sparse dictionary learning, and investigate how a simple null space projection or alternatively regularized least squares-based classification methods can reduce the computational complexity, without sacrificing the detection accuracy, when compared to sparse representation-based classification. (ii) Then we introduce a sparse representation-based domain adaptation technique in order to project other existing users' abnormal and normal signals onto the new user's signal space, enabling us to train the dedicated classifier without having any abnormal heartbeat of the new user. Therefore, zero-shot learning can be achieved without the need for synthetic abnormal heartbeat generation. An extensive set of experiments performed on the benchmark MIT-BIH ECG dataset shows that when this domain adaptation-based training data generator is used with a simple 1-D CNN classifier, the method outperforms the prior work by a significant margin. (iii) Then, by combining (i) and (ii), we propose an ensemble classifier that further improves the performance. This approach for zero-shot arrhythmia detection achieves an average accuracy level of 98.2% and an F1-Score of 92.8%. Finally, a personalized energy-efficient ECG monitoring scheme is proposed using the above-mentioned innovations.

翻译：本文提出一种低成本、高精度的心电图监测系统，旨在为可穿戴移动传感器实现个性化的早期心律失常检测。现有的个性化心电图监测监督方法需要同时使用异常和正常心搏来训练专用分类器。然而，在实际场景中，当个性化算法嵌入可穿戴设备时，对于无心脏病史的健康人群而言，此类训练数据并不可得。在本研究中：（i）我们提出对通过稀疏字典学习获得的健康信号空间进行零空间分析，并探究与基于稀疏表示分类的方法相比，简单的零空间投影或正则化最小二乘分类方法如何在保持检测精度的同时降低计算复杂度。（ii）随后，我们引入一种基于稀疏表示的领域自适应技术，将其他现有用户的异常与正常信号映射至新用户的信号空间，从而无需新用户的任何异常心搏即可训练专用分类器。因此，无需生成合成异常心搏即可实现零样本学习。在标准MIT-BIH心电图数据集上进行的大量实验表明，当这种基于领域自适应的训练数据生成器与简单的一维CNN分类器结合使用时，该方法显著优于现有研究成果。（iii）通过整合（i）与（ii），我们进一步提出能提升性能的集成分类器。这种零样本心律失常检测方法的平均准确率达到98.2%，F1分数为92.8%。最后，基于上述创新技术，我们提出了一种个性化的节能心电图监测方案。