  Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide,
with 2% of the population affected. It is associated with an increased risk of
strokes, heart failure and other heart-related complications. Monitoring
at-risk individuals and detecting asymptomatic AF could result in considerable
public health benefits, as individuals with asymptomatic AF could take
preventive measures with lifestyle changes. With increasing affordability to
wearables, personalized health care is becoming more accessible. These
personalized healthcare solutions require accurate classification of
bio-signals while being computationally inexpensive. By making inferences
on-device, we avoid issues inherent to cloud-based systems such as latency and
network connection dependency. We propose an efficient pipeline for real-time
Atrial Fibrillation Detection with high accuracy that can be deployed in
ultra-edge devices. The feature engineering employed in this research catered
to optimizing the resource-efficient classifier used in the proposed pipeline,
which was able to outperform the best performing standard ML model by
$10^5\times$ in terms of memory footprint with a mere trade-off of 2%
classification accuracy. We also obtain higher accuracy of approximately 6%
while consuming 403$\times$ lesser memory and being 5.2$\times$ faster compared
to the previous state-of-the-art (SoA) embedded implementation.
