Affiliations: Department of Computer Science, ESEI, University of Vigo, Ourense, Spain
Correspondence:
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Corresponding author: María J. Lado, Escola Superior de Enxeñ'aría Informática, Universidade de Vigo, Campus As Lagoas s/n. 32004 Ourense, Spain. E-mail: [email protected].
Abstract: Background:Premature ventricular contractions (PVCs) are cardiac abnormalities that may occur in subjects with/without cardiovascular disorder. Detection is usually performed from electrocardiograms (ECGs); heart activity for a long period of time must be recorded at hospital or with ambulatory electrocardiography. An alternative with a common mobile device would be very interesting, because a simple heart rate sensor should be sufficient. Objective:To develop an algorithm to detect PVCs using the RR-interval (distance between consecutive beats) extracted from ECGs or from the heart rate signal captured by mobile devices. Methods:Feature extraction and classification techniques were included: 1) two timing interval features (prematurity and compensatory pause) were extracted. 2) A linear classifier was applied. To validate the method, the MIT-BIH Arrhythmia Database was used. Considering the existence of unbalanced classes (normal beats and PVCs) at different decision costs, validation was performed with receiver operating characteristic (ROC) analysis. Results:A sensitivity of 90.13% and a specificity percentage of 82.52% were achieved. The area under the ROC curve (AUC) was 0.928. Conclusions:The method is advantageous since it only uses the RR-interval signal for PVC detection, and results compare well with more complex methods that use ECG recording.
Keywords: Heart rate, premature ventricular contractions, pattern recognition, ECG, biomedical signal processing, linear classifiers
