Continuous rhythm monitoring using wearable devices is a potential tool for early identification of atrial fibrillation (AF), the most frequent cardiac arrhythmia (with 0,51% worldwide prevalence, increasing with time), and is also a tool for remote monitoring patients after cardiac surgery. However, AF detection directly through wearable devices is limited by the computational complexity of the classifier model.
In this work we propose a lightweight AF classifier model based on the VGG-11 architecture (LiteVGG-11), focusing on reducing the number of parameters and numerical operations. Using a low number of filters, depthwise separable convolution, and global pooling, this model has only 20,454 parameters and needs 6.9 MFLOP to make an inference for an input of 10 seconds of the ECG leads I and II, sampled at 200 Hz.
To test its effectiveness for AF detection we used the PhysioNet/CinC Challenge 2021 public dataset, stratifying the classes into sinus rhythm, AF, and other rhythms. After 10 Monte Carlo cross-validation splits, with 24,260 unbalanced samples for training and 1,536 balanced samples for validation and testing, the observed metrics (mean ± standard deviation) were: Se 94.1±0.1%; Sp 91.9±0.8%; F1-Score 89.50.7±%; and AUC 96.1±0.6%.