一个36nW电心电图异常探测器基于1.5位非反三角量子计,用于始终启动的心脏监测
IEEE transactions on biomedical circuits and systems
|January 31, 2024
概括
一个超低功率心电图 (ECG) 异常检测器 (EAD) 在心脏监测中达到91.3%的准确性. 这种始终启动的设备使用了新型的三角形定量化和无倍数卷积神经网络 (CNN) 进行连续心律失常检测.
科学领域:
- 生物医学工程 生物医学工程
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 持续的心脏监测对于检测心律失常至关重要.
- 现有系统经常面临电力消耗和复杂性的局限性.
- 需要有效的,始终在线的ECG异常检测解决方案.
研究的目的:
- 提出和评估一个超低功率 (ULP) 始终启动的心电图异常探测器 (EAD).
- 开发一种用于实时心脏监测的新型特征提取和分类引擎.
- 为了证明高检测准确度与最低功耗.
主要方法:
- 实现了一个1.5位的非反三角形定量器 (DQ) 与一个计算在电容器 (CIC) 减法器用于特征提取.
- 使用一个无乘数卷积神经网络 (CNN) 引擎进行事件驱动的分类.
- 集成的DQ和CNN芯片分别采用65nm和180nm的CMOS技术制造.
主要成果:
- 在MIT-BIH心律失常数据库中实现了90.6%91.3%的检测准确度,确定了三个心电图异常.
- 核心电路的超低平均功耗为36nW.
- 消除了对传统高分辨率模拟数字转换器 (ADC) 的需求.
结论:
- 拟议的ULP EAD为持续的心脏监测提供了一个有希望的解决方案.
- 新的DQ和CNN架构使得高效和准确的异常检测成为可能.
- 这项技术代表着随时可穿戴健康设备的重大进步.
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