Detection of ventricular tachycardia and fibrillation using adaptive variational mode decomposition and boosted-CART classifier. (January 2018)

• Record Type: Journal Article
• Authors: Xu, Yang; Wang, Dong; Zhang, Weigong; Ping, Peng; Feng, Lihang
• Is Part Of: Biomedical signal processing and control. Volume 39(2018)
• Page Start: 219
• Page End: 229
• Publication Date: 2018-01
• Subjects: Ventricular arrhythmias detection -- Adaptive variational mode decomposition -- CART -- Cross-validation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.07.031
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: 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. Abstract: 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.