Extended Data Fig. 5: Schematic overview of the DeepRhythmAI model.

The raw ECG signal (in timestamp + mV format) is pre-processed and fed to a single CNN classifier model that identifies the QRS complexes and segments of noisy (non-diagnostic) signals in the raw ECG data. The network components downstream to this module are fed both raw signal and the QRS/noise module output. This combined signal and QRS/noise data are processed by ensemble of a total of 7 models with both wide context (HR trend and morphology of beats) and narrow context (signal details). The wide context module is an ensemble of three custom deep neural network models with both CNN and transformer layers. The narrow context module is an ensemble of three transformer models all based on Vision Transformer ideas but with custom adaptations to 1D multichannel ECG signal. The output from these models is then combined with a wide context asystole filter that has the same architecture as wide context models but with hyperparameters tuned for asystole detection. The asystole filter overrides and replaces the other probabilities when asystoles are detected; otherwise, the output probabilities are averaged.