A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias

Figure 1. Overview of study design and data allocation.

Figure 2. Activation map and fluoroscopic map for ventricular premature complex originating from the right coronary cusp (RCC). The local activation map showed the earliest activation site (−36 ms) at the RCC and catheter ablation in this area (red spots) could eliminate the ventricular arrhythmia.

Figure 3. Data preprocessing and model structure demonstrated with feature maps The upper part of the figure demonstrates a simplified workflow to extract VPC (ventricular premature contraction) waves from the raw data. The 12 cropped VPC waves will be further stacked together to form a 12 × 1024 matrix for model input. The lower part of the figure demonstrates a simplified structure of the model with feature maps passed through the model. The red square is the kernel of each CNN layer. The number of feature maps and the ratio of feature maps are modified for better demonstrations. C1−C6 is the label for each CNN layer, which matches the maker used in Table 2.

Figure 4. Receiver operating characteristic (ROC) of models for classifying ventricular premature contraction and distribution of model output from a different location. (A) The ROC of the model for distinguishing VPC from the left or right ventricle. The red dot indicates the best geometrical mean (g-mean) of sensitivity and specificity. (B) The vertical line of each group indicates the range of the values. The width of the plot indicates the ratio of data with this value. (LV summit: VPC from left ventricle summit, Cusp: VPC from right coronary cusp, left coronary cusp, right and left cusp junction. Left chamber: VPC from the aorto-mitral curtain, Left anterior fascicle, mitral annulus, left ventricular papillary muscle, RVOT: VPC from right ventricle outflow tract. Right chamber: VPC from right ventricular parahisian, pulmonary artery, right ventricular moderator band, tricuspid annulus). (C) The ROC of the model for identifying VPC from the summit of the left ventricle. The red dot indicates the best geometrical mean (g-mean) of sensitivity and specificity.

Figure 5. Change in model performance with the increasing size of training data. Each blue dot indicates model performance with a different training data size. The orange dot indicates the final model with the implementation of weighted sampling and data augmentation. The vertical axis is the area under the curve of the ROC from a different model and the horizontal axis is the data size being used. The unit of the data set shown here is one single VPC wave.

Figure 6. Combining two models for Ventricular Premature Contraction source identification. This workflow shows the performance of combining the use of two models. The percentage shown in the figure indicates the portion of waves being correctly identified. Text and frame with light color indicate false prediction.