Real-time wearable electrocardiogram monitoring sensor is one of the best candidates in assisting cardiovascular disease diagnosis. In this paper, we present a novel real-time machine learning system for Arrhythmia classification. The system is based on the parallel Delta modulation and QRS/PT wave detection algorithms. We propose a patient dependent rotated linear-kernel support vector machine classifier that combines the global and local classifiers, with three types of feature vectors extracted directly from the Delta modulated bit-streams. The performance of the proposed system is evaluated using the MIT-BIH Arrhythmia database. According to the AAMI standard, two binary classifications are performed and evaluated, which are supraventricular ectopic beat (SVEB) versus the rest four classes, and ventricular ectopic beat (VEB) versus the rest. For SVEB classification, the preferred SkP-32 method's F1 score, sensitivity, specificity, and positive predictivity value are 0.83, 79.3%, 99.6%, and 88.2%, respectively, and for VEB classification, the numbers are 0.92%, 92.8%, 99.4%, and 91.6%, respectively. The results show that the performance of our proposed approach is comparable to that of published research. The proposed low-complexity algorithm has the potential to be implemented as an on-sensor machine learning solution.

中文翻译：

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使用并行增量调制和旋转线性核支持向量机的实时心律失常心跳分类算法

实时可穿戴心电监测传感器是辅助心血管疾病诊断的最佳候选之一。在本文中，我们提出了一种用于心律失常分类的新型实时机器学习系统。该系统基于并行 Delta 调制和 QRS/PT 波检测算法。我们提出了一种患者相关的旋转线性核支持向量机分类器，它结合了全局和局部分类器，三种类型的特征向量直接从 Delta 调制比特流中提取。使用 MIT-BIH 心律失常数据库评估所提出系统的性能。根据 AAMI 标准，执行和评估两个二元分类，即室上性异位搏动 (SVEB) 与其余四类，以及室性异位搏动 (VEB) 与其余四类。对于SVEB分类，首选SkP-32方法的F1评分、敏感性、特异性和阳性预测值分别为0.83、79.3%、99.6%和88.2%，而对于VEB分类，数字为0.92%、92.8%、分别为 99.4% 和 91.6%。结果表明，我们提出的方法的性能与已发表的研究相当。所提出的低复杂度算法有可能作为传感器上的机器学习解决方案来实现。结果表明，我们提出的方法的性能与已发表的研究相当。所提出的低复杂度算法有可能作为传感器上的机器学习解决方案来实现。结果表明，我们提出的方法的性能与已发表的研究相当。所提出的低复杂度算法有可能作为传感器上的机器学习解决方案来实现。