School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Biosensors (Basel). 2022 Aug 22;12(8):665. doi: 10.3390/bios12080665.
The respiratory rate is widely used for evaluating a person's health condition. Compared to other invasive and expensive methods, the ECG-derived respiration estimation is a more comfortable and affordable method to obtain the respiration rate. However, the existing ECG-derived respiration estimation methods suffer from low accuracy or high computational complexity. In this work, a high accuracy and ultra-low power ECG-derived respiration estimation processor has been proposed. Several techniques have been proposed to improve the accuracy and reduce the computational complexity (and thus power consumption), including QRS detection using refractory period refreshing and adaptive threshold EDR estimation. Implemented and fabricated using a 55 nm processing technology, the proposed processor achieves a low EDR estimation error of 0.73 on CEBS database and 1.2 on MIT-BIH Polysomnographic Database while demonstrating a record-low power consumption (354 nW) for the respiration monitoring, outperforming the existing designs. The proposed processor can be integrated in a wearable sensor for ultra-low power and high accuracy respiration monitoring.
呼吸频率被广泛用于评估一个人的健康状况。与其他有创和昂贵的方法相比，基于心电图的呼吸估计是一种更舒适、更经济的获取呼吸频率的方法。然而，现有的基于心电图的呼吸估计方法存在精度低或计算复杂度高的问题。在这项工作中，提出了一种高精度、超低功耗的基于心电图的呼吸估计处理器。提出了几种技术来提高精度和降低计算复杂度（从而降低功耗），包括使用可重置的不应期和自适应阈值的 EDR 估计进行 QRS 检测。该处理器采用 55nm 工艺实现和制造，在 CEBS 数据库上的 EDR 估计误差低至 0.73，在 MIT-BIH 多导睡眠图数据库上的 EDR 估计误差低至 1.2，同时在呼吸监测方面实现了创纪录的低功耗（354nw），优于现有设计。该处理器可以集成在可穿戴传感器中，用于超低功耗和高精度的呼吸监测。
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