Automatic ectopic beat elimination in short-term heart rate variability measurement.
Our studies deal with fully automatic measurement of heart rate variability (HRV) in short term electrocardiograms. Presently, all existing HRV analysis programs require user intervention for ectopic beat identification, especially of supraventricular ectopic beats (SVE). This makes the HRV measurement in large, e.g. epidemiological studies problematic. In this paper, we present a fully automatic algorithm for the discrimination of the ventricular (VE) and SVE ectopic beats from the normal QRS complexes suited for a reliable HRV analysis. The QRS identification is based on the template matching method. The ectopic beats are identified based on several morphological and timing properties of the electrocardiogram (ECG) signal. The method incorporates several approaches and makes HRV analysis of large numbers of electrocardiograms feasible. It uses the template matching for the basic morphology check of the QRS complex and the P-wave, the timing information to avoid unnecessary ectopic beat checks and to adjust thresholds and it also looks for a special QRS morphology, which is common in VEs. We used a testing set of 69 electrocardiograms selected from a large number of recordings. The selected ECGs contained abnormalities including ectopic beats, right branch bundle block, respiratory arrhythmia, blocked atrial extrasystole, high amplitude and wide T-waves. The evaluation of our method showed a specificity of 0.99, supraventricular ectopic beat sensitivity of 0.99 and ventricular ectopic beat sensitivity of 0.98.
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Department of Cardiological Sciences, St. George's Hospital Medical School, Cranmer Terrace, SW17 0RE, London, UK.
Savelieva
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Hemingway
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Malik
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D000465:Algorithms; D001331:Automation; D004562:Electrocardiography / Q000379:methods; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors