Implementing the confidence constraint cloud-edge collaborative computing strategy for ultra-efficient arrhythmia monitoring

Abstract

Electrocardiogram(ECG) monitoring is a critical and intricate task in cardiac healthcare. While large models supported by the remote cloud servers with abundant computational resources offers a feasible solution for this task, which still face challenges related to processing costs, privacy risks, and response time. Efforts have been made to integrate edge computing as a supplementary solution, but the current collaborative computing strategy for ECG monitoring is static and inefficient. To address these shortcomings, this study proposes an innovative confidence constraint cloud–edge collaborative computing(3CE2C) strategy. Firstly, the model implementation processes are illustrated, including an ultra-lightweight model for the edge node and a large model for the cloud server. To enhance classification performance, the personalized strategy is employed, resulting in an accuracy improvement from 0.9849 to 0.9929 in the model-cloud. For edge implementation, the optimal input length and model quantization both are explored to reduce the energy consumption. Based on the given confidence constraint, the models dynamically collaborate, with the low-confidence samples uploaded to the cloud server. This approach can achieve accuracy comparable to cloud computing, transmitting only about 17% low-confidence samples, the accuracy ratio(rAcc) is 0.9985. In addition, the method is validated in SVDB, where 3CE2C outperforms state-of-the art framework with the same uploaded sample ratio, resulting in a 1.54% improvement in classification accuracy. In conclusion, the proposed method provides a practical solution in real-time arrhythmia detection applications.