Detecting Wolff-Parkinson-White from Lead-I ECG Using Transfer Learning and Wavelets

Wolff-Parkinson-White (WPW) syndrome is a congenital heart defect that can trigger ventricular fibrillation and sudden cardiac death. Expert inspection of a 12- lead clinical electrocardiogram (ECG) or a Holter record is the standard approach for detecting WPW syndrome. Smartwatches that acquire lead-I ECGs can enable automated detection of WPW episodes in out-of-hospital settings and prevent adverse outcomes. In this work, we explore deep-learning solutions to identify WPW syndromes on lead-I ECG. Scarcity of labeled ECG data for WPW and other cardiac conduction disorders poses a major challenge for training data-driven methods. Moreover, generalizability of such methods to external patient population remains unexplored. To address these challenges, first we implement and compare multiple existing strategies from time-domain augmentations on the lead-I ECG to transfer learning of Imagenet-models for ECG wavelet transformations. Training and holdout validation of these methods are conducted using about 14,000 ECGs from PTB-XL, a publicly available ECG dataset. Moreover, we explore generalization of these methods by external validation on the data from 140 patients from the Tongji Hospital ECG Database. While these methods achieve 88% sensitivity and 99% specificity in identifying a lead-I ECG with evident WPW, and an area under the receiver-operating curve (AUC) of 0.99 on the holdout set from PTB-XL, the sensitivity drops to 58% with an AUC of 0.88 on the external validation. Finally, we propose a novel data augmentation strategy by incorporating labeled data from an umbrella super-class of cardiac conduction disorders, instead of WPW alone, thus naturally reducing the data imbalance for model training. We apply these models as zero-shot transfer learning for discriminating WPW from normal ECG. While this approach achieves similar performance during holdout validation, it also demonstrates strong performance on the external Tongji dataset with sensitivity 0.78 and AUC 0.91. This result shows significant generalizability of the proposed method and highlights the potential of deep-learning solutions in monitoring WPW syndrome with lead-I ECG in outof-hospital general populace settings.