Detection of ventricular tachycardia and fibrillation using adaptive variational mode decomposition and boosted-CART classifier

- An adaptive variational mode decomposition (adaptive-VMD) algorithm is proposed.
- Compare with VMD, the proposed algorithm can better capture the details of ECG.
- No feature selection procedure is needed in the proposed VT/VF detection method.
- Proposed VT/VF detection method achieves an excellent overall performance on public databases.

Rapid ventricular tachycardia (VT) and ventricular fibrillation (VF) are serious life-threatening ventricular arrhythmias. Correct detection of VT/VF is crucial for the rescue of cardiac arrest patient. In this paper, we proposed a new method for improving the detection effect of VT/VF. An adaptive variational mode decomposition (adaptive-VMD) algorithm was presented to decompose the electrocardiogram (ECG) signal into five band-limited intrinsic modes (BLIMs). Then, a total of 6 features were extracted from these BLIMs to characterize the details of VT/VF. Last, a boosted classification and regression tree (Boosted-CART) classifier that combines feature selection and recognition was used to detect VT/VF. Three annotated public ECG databases were used as the training and testing datasets. Ten-fold cross-validation was implemented to assess the performance of the method. An accuracy (Acc) of 98.29% ± 0.18%, a sensitivity (SE) of 97.32% ± 0.12% and a specificity (SP) of 98.95% ± 0.84% were obtained. In comparison with the existing state-of-the-art methods for VT/VF detection, the proposed method demonstrated better overall performance.