Key result
A PVC classification method based on fractional order system modeling achieved an overall accuracy of 94.745%, specificity of 95.178%, and sensitivity of 90.021%.
Why the study?
Automatic characterization of premature ventricular contractions is important for early diagnosis of life-threatening cardiac diseases.
Does a fractional order system modeling algorithm accurately classify normal and PVC beats in ECG signals?
Does a fractional order system modeling algorithm accurately classify normal and PVC beats in ECG signals?
A novel classification scheme using fractional order system modeling of the QRS complex achieved 94.7% accuracy in discriminating normal and PVC beats.
It is well known that many physiological phenomena are modeled accurately and effectively using fractional operators and systems. This type of modeling is due mainly to the dynamical link between fractional-order systems and the fractal structures of the physiological systems. The automatic characterization of the premature ventricular contraction (PVC) is very important for early diagnosis of patients with different life-threatening cardiac diseases. In this paper, a classification scheme of normal and PVC beats of the electrocardiogram (ECG) signal is proposed. The clustering features used for normal and PVC beats discrimination are the parameters of the commensurate order linear fractional model of the frequency content of the QRS complex of the ECG signal. A series of tests and comparisons have been performed to evaluate and validate the efficiency of the proposed PVC classification algorithm using the MIT-BIH arrhythmia database. The proposed PVC classification method has achieved an overall accuracy of 94.745%, a specificity of 95.178% and a sensitivity of 90.021% using all the 48 records of the database.
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Assadi et al. (2021) studied Premature ventricular contraction (PVC). PVC classification algorithm based on fractional order system modeling was evaluated on Overall accuracy of PVC classification. A PVC classification method based on fractional order system modeling achieved an overall accuracy of 94.745%, specificity of 95.178%, and sensitivity of 90.021%.
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