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. 2022 Aug 22;12(8):665.
doi: 10.3390/bios12080665.

A High Accuracy & Ultra-Low Power ECG-Derived Respiration Estimation Processor for Wearable Respiration Monitoring Sensor

Jiajing Fan  1 , Siqi Yang  1 , Jiahao Liu  1 , Zhen Zhu  1 , Jianbiao Xiao  1 , Liang Chang  1 , Shuisheng Lin  1 , Jun Zhou  1
Affiliations

A High Accuracy & Ultra-Low Power ECG-Derived Respiration Estimation Processor for Wearable Respiration Monitoring Sensor

Jiajing Fan et al. Biosensors (Basel). .

Abstract

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.

Keywords: EDR; QRS detection; processor; wearable respiration monitoring sensor.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Illustration of EDR estimation principles. (a) Time-domain method. (b) Frequency-domain method.
Figure 2
Figure 2
Block diagram of proposed QRS complex detection method.
Figure 3
Figure 3
The R peak is blocked by P wave due to refractory period.
Figure 4
Figure 4
The flow diagram of threshold-based judgement with improved refractory period mechanism.
Figure 5
Figure 5
EDR estimation using the counting methods. (a,c) Threshold calculated using maximum value. (b,d) Threshold calculated using the average value.
Figure 6
Figure 6
EDR estimation method flowchart.
Figure 7
Figure 7
EDR segment and threshold.
Figure 8
Figure 8
The die photo of the proposed EDR estimation processor (EDREP).
Figure 9
Figure 9
Architecture of the proposed EDR estimation processor.
Figure 10
Figure 10
Test setup. (a) Block diagram. (b) Photo of the environment.
See this image and copyright information in PMC

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