On-device edge-learning for cardiac abnormality detection using a bio-inspired and spiking shallow network

Abstract

This work introduces on-device edge learning for cardiac abnormality detection by merging spiking 2D Convolutional Long-Short-Term Memory (ConvLSTM2D) with a bio-inspired shallow neural network, referred to as Closed-form Continuous-time (CfC), to form the sCCfC model. The model achieves an F1 score and AUROC of 0.82 and 0.91 in cardiac abnormalities detection. These results are comparable to the non-spiking ConvLSTM2D–CfC (ConvCfC) model [Huang et al., J. Cardiovasc. Transl. Res. (published online, 2024)]. Notably, the sCCfC model demonstrates a significantly higher energy efficiency with an estimated power consumption of 4.68 μJ/Inf (per inference) on an emulated Loihi’s neuromorphic chip architecture, in contrast to ConvCfC model’s consumption of 450 μJ/Inf on a conventional processor. In addition, as a proof-of-concept, we deployed the sCCfC model on the conventional and relatively resource-constrained Radxa Zero, which is equipped with an Amlogic S905Y2 processor for on-device training, which resulted in performance improvements. After initial training of two epochs on a conventional Graphics Processing Unit, the F1 score and AUROC improved from 0.46 and 0.65 to 0.56 and 0.73, respectively, with five additional epochs of on-device training. Furthermore, when presented with a new dataset, the sCCfC model showcases strong out-of-sample generalization capabilities that can constitute a pseudo-perspective test, achieving an F1 score and AUROC of 0.71 and 0.86, respectively. The spiking sCCfC also outperforms the non-spiking ConvCfC model in robustness regarding effectively handling missing electrocardiogram (ECG) channels during inference. The model’s efficacy extends to single-lead ECG analysis, demonstrating reasonable accuracy in this context, while the focus of our work has been on the computational and memory complexities of the model.