An artificial neural network using heart rate and respiratory rate variability predicted ventricular tachycardia one hour before onset with 0.88 sensitivity, 0.82 specificity, and an AUC of 0.93.
Case-Control (n=104)
No
Does an artificial neural network model using heart rate and respiratory rate variability parameters accurately predict ventricular tachycardia one hour before occurrence in cardiovascular ICU patients?
An artificial neural network using heart rate and respiratory rate variability parameters demonstrated high accuracy in predicting ventricular tachycardia one hour before its onset in cardiovascular ICU patients.
Effect estimate: AUC 0.93
Ventricular tachycardia (VT) is a potentially fatal tachyarrhythmia, which causes a rapid heartbeat as a result of improper electrical activity of the heart. This is a potentially life-threatening arrhythmia because it can cause low blood pressure and may lead to ventricular fibrillation, asystole, and sudden cardiac death. To prevent VT, we developed an early prediction model that can predict this event one hour before its onset using an artificial neural network (ANN) generated using 14 parameters obtained from heart rate variability (HRV) and respiratory rate variability (RRV) analysis. De-identified raw data from the monitors of patients admitted to the cardiovascular intensive care unit at Asan Medical Center between September 2013 and April 2015 were collected. The dataset consisted of 52 recordings obtained one hour prior to VT events and 52 control recordings. Two-thirds of the extracted parameters were used to train the ANN, and the remaining third was used to evaluate performance of the learned ANN. The developed VT prediction model proved its performance by achieving a sensitivity of 0.88, specificity of 0.82, and AUC of 0.93.
Lee et al. (Fri,) conducted a case-control in Ventricular tachycardia (n=104). Artificial neural network (ANN) prediction model vs. Control recordings was evaluated on Prediction of ventricular tachycardia one hour before onset (AUC 0.93). An artificial neural network using heart rate and respiratory rate variability predicted ventricular tachycardia one hour before onset with 0.88 sensitivity, 0.82 specificity, and an AUC of 0.93.