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：本文建议建立一个低成本和高度准确的ECG监测系统,用于对可磨损的移动传感器进行个人化精密早期心律失常检测。个人化ECG早期监督监测方法要求对专用分类器的培训有异常和正常的心跳。然而,在个人化算法嵌入可磨损装置的现实世界情景中,没有心血管紊乱史的健康人无法获得这种培训数据。在这项研究中,(一)我们建议对通过稀疏字典学习获得的健康信号空间进行空空格分析,并调查简单空投或常规化的基于最小正方位的分类方法如何能够降低计算复杂性,而不会牺牲检测准确性,而与分散的基于代表性的分类方法相比。 (二)然后,我们引入一个空泛的基于代表性的域适应技术,以便将其他现有用户的异常和正常信号投放到新的用户的信号空间中,使我们能够在没有新用户任何异常的心跳的情况下对专门分类器进行培训。因此,可以在不需要合成异常的心跳生成的情况下实现零点学习。在基准D-BIL8上进行广泛的测试,而无需牺牲精确精确精确精确精确的精确度,在基准上的精确度上,在使用前一级进行一项前一级GG数据显示,然后使用一种重要的标准。