A predictor for ventricular tachycardia based on heart rate variability analysis

A classifier that predicts ventricular tachycardia (VT) events using artificial neural networks (ANNs) trained with parameters extracted from heart rate variability (HRV) analysis and principal component analysis (PCA) was developed in this paper. The Spontaneous Ventricular Tachyarrhythmia Database (Medtronic Version 1.0), comprising 106 pre-VT records and 126 control data, was used. Each data set was subjected to preprocessing and parameter extraction. After correcting the ectopic beats, data in the 5 minute window prior to the 10 second duration of each event was cropped for parameter extraction. Extraction of the time domain and parameters of Poincaré plot was performed subsequently. After reducing dimension of the feature database with PCA, two-thirds of the database was used to train the ANN, and the remainder was used to verify the performance. ANNs for classifying the VT events was developed, and the sensitivity of the ANN was 74.3% (66.7% specificity). The normalized area under the receiver operating characteristic (ROC) curve of the ANN was 0.72.