对于可穿戴心脏监测设备的一种节能可配置的1D CNN-based多电图分类协处理器
IEEE transactions on biomedical circuits and systems
|March 3, 2025
概括
这项研究引入了一种新的多导电心电图 (ECG) 协处理器,用于有效检测心脏异常. 该设计提供卓越的能源效率和可配置性,用于先进的ECG分析.
科学领域:
- 生物医学工程 生物医学工程
- 计算机工程 计算机工程
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 处理器对于心脏监测至关重要,但经常受到低能效和有限的配置性影响.
- 现有的系统在处理不同数量的线索和调整推理模型方面缺乏灵活性.
研究的目的:
- 提出一种新型的多电图共处理器,用于高效和可配置的心脏异常检测.
- 为了提高R峰检测灵敏度和精度.
- 在ECG处理中实现高能效.
主要方法:
- 开发了用于R峰检测的零交叉斜率自适应值比较方法.
- 设计了一个可重新配置的一维卷积神经网络 (1-D CNN) 分类引擎,利用并行性和数据重用策略.
- 实现了一个基于单指令多数据 (SIMD) 的中央控制器,以增强配置性.
主要成果:
- 拟议的ECG共处理器实现了超过98%的分类准确度.
- 该芯片表现出极高的能源效率,在100MHz时只消耗62.2nJ.
- 能源效率达到了397.1GOPS/W,比现有的基于CNN的ECG处理器提高了40%.
结论:
- 与当前技术相比,新型ECG共处理器在能源效率和可配置性方面提供了显著的优势.
- 这种设计使得心脏异常检测更加有效和适应性.
- 该协处理器适用于先进的低功耗心脏监测应用.
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