A Noisy Beat is Worth 16 Words: a Tiny Transformer for Low-Power
  Arrhythmia Classification on Microcontrollers
Paola Busia Matteo Antonio Scrugli Victor Jean-Baptiste Jung Luca Benini Paolo Meloni
Wearable systems for the long-term monitoring of cardiovascular diseases are
becoming widespread and valuable assets in diagnosis and therapy. A promising
approach for real-time analysis of the electrocardiographic (ECG) signal and
the detection of heart conditions, such as arrhythmia, is represented by the
transformer machine learning model. Transformers are powerful models for the
classification of time series, although efficient implementation in the
wearable domain raises significant design challenges, to combine adequate
accuracy and a suitable complexity. In this work, we present a tiny transformer
model for the analysis of the ECG signal, requiring only 6k parameters and
reaching 98.97% accuracy in the recognition of the 5 most common arrhythmia
classes from the MIT-BIH Arrhythmia database, assessed considering 8-bit
integer inference as required for efficient execution on low-power
microcontroller-based devices. We explored an augmentation-based training
approach for improving the robustness against electrode motion artifacts noise,
resulting in a worst-case post-deployment performance assessment of 98.36%
accuracy. Suitability for wearable monitoring solutions is finally demonstrated
through efficient deployment on the parallel ultra-low-power GAP9 processor,
where inference execution requires 4.28ms and 0.09mJ.