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

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 estimatio...

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Bibliographic Details
Published in:Biosensors (Basel) 2022-08, Vol.12 (8), p.665
Main Authors: Fan, Jiajing, Yang, Siqi, Liu, Jiahao, Zhu, Zhen, Xiao, Jianbiao, Chang, Liang, Lin, Shuisheng, Zhou, Jun
Format: Article
Language:English
Subjects:
Accuracy
Assessment
Biosensors
Complexity
Computer applications
EDR
Electrocardiogram
Electrocardiography
Health aspects
Methods
Microprocessors
Monitoring
Neural networks
Noise
Patient monitoring
Physiology
Power consumption
Power management
processor
QRS detection
Refractory period
Respiration
Respiratory organs
Respiratory rate
Signal processing
Wavelet transforms
Wearable computers
wearable respiration monitoring sensor
Wearable technology
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Summary: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.
ISSN:2079-6374
2079-6374
DOI:10.3390/bios12080665