Data from: Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

Implantable-cardioverter defibrillators (ICD) detect and terminate
life-threatening ventricular tachyarrhythmia with electric shocks after
they occur. This puts patients at risk if they are driving or in a
situation where they can fall. ICD’s shocks are also very painful and
affect a patient’s quality of life. It would be ideal if ICDs can
accurately predict the occurrence of ventricular tachyarrhythmia and then
issue a warning or provide preventive therapy. Our study explores the use
of ICD data to automatically predict ventricular arrhythmia using heart
rate variability (HRV). A 5 minute and a 10 second warning system are both
developed and compared. The participants for this study consist of 788
patients who were enrolled in the ICD arm of the Sudden Cardiac Death –
Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular
heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed
and different HRV features are extracted. Machine learning algorithms,
including random forests (RF) and support vector machines (SVM), are
trained on these features to classify the two groups of rhythms in a
subset of the data comprising the training set. These algorithms are then
used to classify rhythms in a separate test set. This performance is
quantified by the area under the curve (AUC) of the ROC curve. Both RF and
SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean
AUC of 0.87-0.88 for 10-second prediction; an AUC over 0.8 typically
warrants further clinical investigation. Our work shows that moderate
classification accuracy can be achieved to predict ventricular
tachyarrhythmia with machine learning algorithms using HRV features from
ICD data. These results provide a realistic view of the practical
challenges facing implementation of machine learning algorithms to predict
ventricular tachyarrhythmia using HRV data, motivating continued research
on improved algorithms and additional features with higher predictive
power.