  Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated
with a high risk for serious conditions like stroke. The use of wearable
devices embedded with automatic and timely AF assessment from
electrocardiograms (ECGs) has shown to be promising in preventing
life-threatening situations. Although deep neural networks have demonstrated
superiority in model performance, their use on wearable devices is limited by
the trade-off between model performance and complexity. In this work, we
propose to use lightweight convolutional neural networks (CNNs) with
parameterised hypercomplex (PH) layers for AF detection based on ECGs. The
proposed approach trains small-scale CNNs, thus overcoming the limited
computing resources on wearable devices. We show comparable performance to
corresponding real-valued CNNs on two publicly available ECG datasets using
significantly fewer model parameters. PH models are more flexible than other
hypercomplex neural networks and can operate on any number of input ECG leads.
