Energy-efficient Spike Encoding for ECG Arrhythmia Classification

Abstract

Cardiovascular diseases (CVDs) are the top cause of death worldwide, and their diagnosis can be quickly and painlessly achieved through Electrocardiogram (ECG). The diagnosis of electrocardiogram has gradually evolved from manual diagnosis by doctors to one that can be realized using Artificial Intelligence (AI). Early AI still required manual extraction of features for ECG classification, but later Deep Neural Network (DNN) could automatically extract features during the learning process. With this technology, people can monitor heart movements in real-time through wearable devices. If there are any abnormalities, they can seek medical treatment in time to prevent death from sudden severe heart disease. However, for the wearable device, the energy consumption per classification by the traditional AI is so high that a limited battery cannot work for a long time.

To address this issue, this thesis adopts Spiking Neural Network (SNN) to do classification and implement the inference on the hardware. Compared with traditional Artificial Neural Network (ANN), SNN is highly energy efficient. According to the needs of SNN and its event-driven characteristics, the multi-threshold-based encoding scheme is proposed, which encodes the heartbeat into 54 spikes on average with less information loss. The SNN model is trained by ANN-SNN conversion with an accuracy of 97.42\%. After RTL coding, synthesis, and back-end implementation, the chip with encoding and inference functions achieves an energy consumption of only 57.88 nJ per heartbeat classification.