Open AccessPrediction of Ventricular Tachycardia One Hour before Occurrence Using Artificial Neural Networks
Author(s) -
Hyojeong Lee,
Soo-Yong Shin,
Myung-Ji Seo,
GiByoung Nam,
Segyeong Joo
Publication year - 2016
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep32390
Subject(s) - asystole , ventricular tachycardia , medicine , ventricular fibrillation , cardiology , heartbeat , heart rate , heart rate variability , blood pressure , tachycardia , cardiorespiratory fitness , artificial neural network , machine learning , computer science , computer security
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.