  Idiopathic ventricular arrhythmia (IVAs) is extra abnormal heartbeats
disturbing the regular heart rhythm that can become fatal if left untreated.
Cardiac catheter ablation is the standard approach to treat IVAs, however, a
crucial prerequisite for the ablation is the localization of IVAs' origin. The
current IVA localization techniques are invasive, rely on expert
interpretation, or are inaccurate. In this study, we developed a new
deep-learning algorithm that can automatically identify the origin of IVAs from
ECG signals without the need for expert manual analysis. Our developed deep
learning algorithm was comprised of a spatial fusion to extract the most
informative features from multichannel ECG data, temporal modeling to capture
the evolving pattern of the ECG time series, and an attention mechanism to
weigh the most important temporal features and improve the model
interpretability. The algorithm was validated on a 12-lead ECG dataset
collected from 334 patients (230 females) who experienced IVA and successfully
underwent a catheter ablation procedure that determined IVA's exact origins.
The proposed method achieved an area under the curve of 93%, an accuracy of
94%, a sensitivity of 97%, a precision of 95%, and an F1 score of 96% in
locating the origin of IVAs and outperformed existing automatic and
semi-automatic algorithms. The proposed method shows promise toward automatic
and noninvasive evaluation of IVA patients before cardiac catheter ablation.
