Resource-Efficient CNN Based Atrial Fibrillation Detection Using P-Wave and RR Interval Features for Edge-AI Cardiac Health Monitoring Devices

Atrial fibrillation (AF) is a cardiac arrhythmia which leads to ischemic stroke. This article presents a real-time AF detection method with reduction in energy consumption and false alarms for wearable devices. The method is designed with a 1-D-convolutional neural network (CNN), RR interval (RRI), and P-wave features. The training dataset contains the Fourier magnitude spectrum (FMS) and RRI of 10-s electrocardiogram (ECG) segments from five benchmark ECG databases (1-lead, 2-lead, and 12-lead). The 1-D-CNN-based AF detection methods are tested with two untrained datasets (2-lead and 12-lead) and 20% of the trained dataset. The optimal trade-off between performance and computational complexity is achieved using 5-layer-CNN model (activation function: exponential linear unit and kernel size: 4 $\times$ 1) with a model size of 4.33 MB and processing time of 0.100 ms. The CNN-RRI-FMS based AF detection method has an overall accuracy, sensitivity, and specificity of 99.44%, 98.76%, and 99.81%, respectively. The method is validated on Raspberry-Pi. The method has an average latency and energy consumption of 3.52 ms and 10.76 mJ for a 10-s ECG segment on Raspberry Pi. Comparative analysis with prior studies and existing deep-learning networks signifies the superiority of the method in terms of performance, computational complexity, and energy efficiency. The experimental results emphasize its suitability for real-time implementations in cardiac health monitoring devices.