Using a local reference set for the Kth nearest-neighbours rule achieved 96.7% specificity and 96.9% sensitivity for premature ventricular contraction classification.
Heartbeats annotated as normal or premature ventricular contraction from 48 ECG recordings in the MIT-BIH arrhythmia database.
Kth nearest-neighbours rule with local reference set vs Global reference set
Classification specificity and sensitivity
Absolute Event Rate: 96.7% vs 75.4%
An analysis of electrocardiographic pattern recognition parameters for premature ventricular contraction (PVC) and normal (N) beat classification is presented. Twenty-six parameters were defined: 11 x 2 for the two electrocardiogram (ECG) leads, width of the complex and three parameters derived from a single-plane vectorcardiogram (VCG). Some of the parameters include amplitudes of maximal positive and maximal negative peaks, area of absolute values, area of positive values, area of negative values, number of samples with 70% higher amplitude than that of the highest peak, amplitude and angle of the QRS vector in a VCG plane. They were measured for all heartbeats annotated as N or PVC in all 48 ECG recordings of the MIT-BIH arrhythmia database. Two reference sets for the Kth nearest-neighbours rule were used-global and local. The classification indices obtained with the global reference set were 75.4% specificity and 80.9% sensitivity. Using the local reference set we increased the specificity to 96.7% and the sensitivity to 96.9%. The achieved specificity and sensitivity are comparable with, and greater than, the results reported in the literature.
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Christov et al. (Thu,) conducted a other in Premature ventricular contraction (n=48). Kth nearest-neighbours rule with local reference set vs. Global reference set was evaluated on Classification specificity and sensitivity. Using a local reference set for the Kth nearest-neighbours rule achieved 96.7% specificity and 96.9% sensitivity for premature ventricular contraction classification.
synapsesocial.com/papers/6a22cdf81620e33eec5dcb33 — DOI: https://doi.org/10.1088/0967-3334/26/1/011
Ivaylo Christov
Bulgarian Academy of Sciences
Irena Jekova
Bulgarian Academy of Sciences
G. Bortolan
Neuroscience Institute
Physiological Measurement
Bulgarian Academy of Sciences
Institute for Biomedical Research and Innovation
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