一个1.69μJ非常强大的心律失常监测处理器,配有三重适应QRS探测器和医疗驱动的功能融合混合神经网络
IEEE transactions on biomedical circuits and systems
|January 22, 2026
概括
这项研究引入了一个强大的处理器,用于可穿戴的心脏监测,即使有噪音的心电图 (ECG) 信号,也能准确检测心律失常. 该系统在低功耗的情况下实现了高精度的R峰检测和心律失常分类.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康技术技术 心血管健康技术
- 信号处理 信号处理
背景情况:
- 来自可穿戴设备的心电图 (ECG) 信号容易产生噪音,阻碍了准确的心律失常检测.
- 诸如运动,基线流浪和肌肉干扰等人工物会损害信号完整性.
- 在现实应用中,强大的信号处理对于可靠的心脏健康监测至关重要.
研究的目的:
- 开发一款非常强大的心脏健康监测处理器,用于从噪声的心电图信号中准确检测心律失常.
- 为了提高可穿戴心脏监测系统的可靠性.
- 为了提高心律失常分类的准确性和计算效率.
主要方法:
- 提出了一种级联的三重适应QRS探测器,具有事件驱动采样,用于在噪声信号中准确识别QRS复合体.
- 开发了一种混合神经网络 (HNN) 分类器,将LSTM和ANN架构与病理特征融合集成.
- 使用65纳米CMOS工艺制造了一种原型,用于性能评估.
主要成果:
- 实现了低功耗 (2.53μW总量,0.072μW动态) 和一个紧的0.99mm2面积.
- 在MIT-BIH噪音应力测试数据库中显示出高R峰检测灵敏度 (>97.38%) 和精度 (>97.08%).
- 在低SNR条件下,在MIT-BIH心律失常数据库中超过了90.1%的患者间心律失常分类准确度.
结论:
- 开发的处理器提供了一种强大的解决方案,用于检测可穿戴设备发出的噪音高的心电图信号中的心律失常.
- 混合神经网络方法提供了高效和准确的心律失常分类.
- 该系统的低功耗和计算复杂性使其适合集成到可穿戴健康监测设备中.
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