Key result
A sparse auto-encoder and softmax regression classifier differentiated premature ventricular contractions from non-PVC rhythms with an overall accuracy of 99.4% and sensitivity of 97.9%.
Why the study?
Does a feature extraction method based on a sparse auto-encoder and softmax regression classifier accurately diagnose premature ventricular contractions from ECG records?
Does a feature extraction method based on a sparse auto-encoder and softmax regression classifier accurately diagnose premature ventricular contractions from ECG records?
A novel machine learning method using a sparse auto-encoder and softmax regression can accurately differentiate premature ventricular contractions from other rhythms on ECG.
Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.
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Yang et al. (2015) studied Premature ventricular contraction (n=40). Sparse auto-encoder (SAE) and softmax regression (SR) classifier was evaluated on Overall accuracy. A sparse auto-encoder and softmax regression classifier differentiated premature ventricular contractions from non-PVC rhythms with an overall accuracy of 99.4% and sensitivity of 97.9%.
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