Fast Detection of Ventricular Fibrillation and Ventricular Tachycardia in 1-Lead ECG from Three-Second Blocks

Background: Ventricular tachycardia (VT) is dangerous irregularity of heart rhythm. VT may evolve into ventricular fibrillation (VF) which often leads to cardiac death. Therefore, fast automated detection of VF/VT events is of the utmost importance. Here, we present a method detecting VT and ventricular fibrillation (VF) events suitable for real-time application on continuously incoming ECG data. Method: We designed a method for detection of VF/VT events in short-time (3 s), 1-lead ECG blocks. Five features are extracted from this block using analysis of ECG spectra, derivatives, amplitude measures and autocorrelation. The extracted features are fed into a logistic regression model showing the probability of a VF/VT event. The model was trained on the public PhysioNet CUDB dataset consisting of 393 automatically selected blocks. Results: The model (AUC 0.99) showed a sensitivity and specificity of 95% and 97%, respectively (5-fold cross-validation). The model was tested on the public PhysioNet VFDB dataset, showing specificity and sensitivity of 95% and 83%, respectively. Both the feature extraction code (Matlab format) and the model are publicly accessible and easy implementation of the logistic regression model predetermines it for real-time applications.