Can a deep neural network accurately predict the heart rate corrected QT interval (QTc) from a multilead smartphone-enabled ECG?
A deep neural network is being developed to predict QTc intervals from a handheld smartphone-enabled ECG device to enable early identification of patients at risk for malignant arrhythmias.
Prolongation of the heart rate corrected QT interval (QTc) can trigger malignant arrhythmias leading to cardiac arrest and death. A handheld ECG device recording leads I and II may enable early identification of patients with prolonged QTc. The objective was to develop a deep neural network (DNN) to
Schram et al. (Fri,) studied this question.