title: Prediction of Ventricular Tachycardia One Hour before Occurrence Using Artificial Neural Networks.
abstract: 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.
authors: Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea.
authors: Hyojeong
authors: H
authors: Lee
authors: Department of Biomedical Informatics, Asan Medical Center, Seoul, Republic of Korea.
authors: Soo-Yong
authors: SY
authors: Shin
authors: Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
authors: Myeongsook
authors: M
authors: Seo
authors: Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
authors: Gi-Byoung
authors: GB
authors: Nam
authors: Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea.|Department of Biomedical Engineering, Asan Medical Center, Seoul, Republic of Korea.
authors: Segyeong
authors: S
authors: Joo
mesh_terms: D042241:Early Diagnosis; D006339:Heart Rate; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods; D016571:Neural Networks, Computer; D011379:Prognosis; D015203:Reproducibility of Results; D056152:Respiratory Rate; D012680:Sensitivity and Specificity; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000503:physiopathology / Q000517:prevention & control*