Towards Efficient ECG-based Atrial Fibrillation Detection via Parameterised Hypercomplex Neural Networks

Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a higher risk for serious conditions like stroke. Long-term recording of the electrocardiogram (ECG) with wearable devices embedded with an automatic and timely evaluation of AF helps to avoid life-threatening situations. However, the use of a deep neural network for auto-analysis of ECG on wearable devices is limited by its complexity. In this work, we propose lightweight convolutional neural networks (CNNs) for AF detection inspired by the recently proposed parameterised hypercomplex (PH) neural networks. Specifically, the convolutional and fully-connected layers of a real-valued CNN are replaced by PH convolutions and multiplications, respectively. PH layers are flexible to operate in any channel dimension n and able to capture inter-channel relations. We evaluate PH-CNNs on publicly available databases of dynamic and in-hospital ECG recordings and show comparable performance to corresponding real-valued CNNs while using approx. $1/n$ model parameters.