Key result
A novel real-time machine learning system using parallel Delta modulation and rotated linear-kernel SVM achieved F1 scores of 0.83 for SVEB and 0.92 for VEB classification.
Why the study?
Real-time wearable ECG monitoring sensors are strong candidates for cardiovascular disease diagnosis, motivating the development of low-complexity, real-time machine learning algorithms for arrhythmia classification.
Does a novel real-time machine learning system using parallel Delta modulation and rotated linear-kernel SVMs accurately classify arrhythmias in ECG recordings?
Does a novel real-time machine learning system using parallel Delta modulation and rotated linear-kernel SVMs accurately classify arrhythmias in ECG recordings?
A novel, low-complexity machine learning algorithm demonstrates high accuracy for real-time arrhythmia classification, highlighting its potential for implementation in wearable ECG monitoring sensors.
May enable real-time wearable ECG monitoring; leaves open prospective clinical validation.
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.
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Tang et al. (2019) studied Arrhythmia. Real-time machine learning system using parallel Delta modulation and rotated linear-kernel SVM was evaluated on Arrhythmia classification (SVEB and VEB). A novel real-time machine learning system using parallel Delta modulation and rotated linear-kernel SVM achieved F1 scores of 0.83 for SVEB and 0.92 for VEB classification.
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