A 1.3 μW Event-Driven ANN Core for Cardiac Arrhythmia Classification in Wearable Sensors

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Why the study?

Wearable sensors require significant power savings, which can be achieved with event-driven systems where power consumption tracks input activities.

Authors

Discussion

May enable ultra-low-power wearable arrhythmia detection; hypothesis-generating pending clinical validation.

Intervention

Event-driven artificial neural network (ANN) core for cardiac arrhythmia classification in wearable sensors

Outcome

Power consumption and classification accuracy

The proposed event-driven ANN core for wearable sensors achieves high accuracy for cardiac arrhythmia classification with very low power consumption.

Cai et al. (2021) studied this question.

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