用提前退出机制让可穿戴设备更省电，实时分类心电数据
DCentNet: Decentralized Multistage Biomedical Signal Classification using Early Exits
- 将模型拆成多阶段，关键节点提前判断并停止计算
- 单个提前退出点节省94.54%传输量，双退出点准确率达97.74%
- 适合资源受限的边缘设备，特别适合长期心电监测
DCentNet是一种新型去中心化多阶段生物医学信号分类方法，专为物联网可穿戴传感器采集的生物信号设计。通过引入早期退出点（EEP），在保证精度的前提下显著提升能效与处理速度。该方法将单一CNN模型分解为多个子网络，在各EEP处采用编码器-解码器结构压缩大尺寸特征图，大幅减少无线传输数据量与功耗。若输入在某阶段即可被自信分类，则提前终止处理。初始子网络可部署于雾端或边缘设备以进一步降低能耗。使用遗传算法优化EEP位置，平衡性能与复杂度。在心电图（ECG）分类实验中，单个EEP使无线数据传输减少94.54%，计算复杂度下降21%，且保持原始准确率与灵敏度；两个EEP时，灵敏度达98.36%，准确率为97.74%，传输量减少91.86%，复杂度下降22%。在ARM Cortex-M4 MCU上实现，相比连续无线传输平均节省73.6%功耗。
原文摘要 · Abstract (English)
DCentNet is a novel decentralized multistage signal classification approach designed for biomedical data from IoT wearable sensors, integrating early exit points (EEP) to enhance energy efficiency and processing speed. Unlike traditional centralized processing methods, which result in high energy consumption and latency, DCentNet partitions a single CNN model into multiple sub-networks using EEPs. By introducing encoder-decoder pairs at EEPs, the system compresses large feature maps before transmission, significantly reducing wireless data transfer and power usage. If an input is confidently classified at an EEP, processing stops early, optimizing efficiency. Initial sub-networks can be deployed on fog or edge devices to further minimize energy consumption. A genetic algorithm is used to optimize EEP placement, balancing performance and complexity. Experimental results on ECG classification show that with one EEP, DCentNet reduces wireless data transmission by 94.54% and complexity by 21%, while maintaining original accuracy and sensitivity. With two EEPs, sensitivity reaches 98.36%, accuracy 97.74%, wireless data transmission decreases by 91.86%, and complexity is reduced by 22%. Implemented on an ARM Cortex-M4 MCU, DCentNet achieves an average power saving of 73.6% compared to continuous wireless ECG transmission.
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