Key result
A real-time ECG classification algorithm using fractal and cross-correlation analyses achieved 100% classification accuracy for normal and premature ventricular contraction beats in some ECG records.
Why the study?
Does an algorithm based on fractal and cross correlation analyses accurately classify ECG beats in real-time?
Does an algorithm based on fractal and cross correlation analyses accurately classify ECG beats in real-time?
An algorithm based on fractal and cross correlation analyses can achieve high accuracy for real-time ECG classification of normal and premature ventricular contraction beats.
May support real-time PVC detection in select ECGs; leaves open prospective validation in diverse clinical datasets.
The objective of this project is to develop algorithms for real-time electrocardiogram (ECG) classification. The program is made to be concise and accurate enough for use on an ambulatory monitor. A reliable QRS detection algorithm based on a one-pole filter has been developed. Automatic ECG classification using fractal and cross correlation analyses is investigated. The computation demand is not high and real-time analysis is possible. Testing is carried out using the American Heart Association (AHA) ventricular arrhythmia ECG data. The types of beat being selected in the study are: normal (N), premature ventricular contraction (V), and fusion of ventricular and normal beats (F). The classification accuracy of 100% for N and V beats can be achieved in some ECG records.
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Lai et al. (2002) studied Ventricular arrhythmia. Real-time ECG classification algorithm based on fractal and cross correlation analyses was evaluated on Classification accuracy for normal (N) and premature ventricular contraction (V) beats. A real-time ECG classification algorithm using fractal and cross-correlation analyses achieved 100% classification accuracy for normal and premature ventricular contraction beats in some ECG records.
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