Key result
Extracting respiration rate from wrist ECG signals demonstrated high accuracy compared to a commercial respiration monitor across 5 different breathing rates.
Why the study?
Continuous respiratory monitoring using a dedicated respiration sensor is inconvenient and expensive.
Does the proposed method accurately extract respiration rate from wrist ECG signals compared to a commercial respiration monitor?
Does the proposed method accurately extract respiration rate from wrist ECG signals compared to a commercial respiration monitor?
A novel method to extract respiration rate from wrist ECG signals shows high accuracy compared to commercial monitors, offering a low-cost alternative for continuous respiratory monitoring without dedicated sensors.
Hypothesis-generating for wrist-based respiration monitoring; prospective clinical validation required before adoption.
Respiratory behavior is one of the important parameters that indicate any physiological changes in human body. However, using a respiration sensor device for continuous monitoring is inconvenient and expensive. In this paper, an approach to acquire the respiration signal from the wrist electrocardiogram (ECG) is proposed. An analog front end (AFE) sampled at 100 Hz is used to collect ECG signals from the wrist to compute and verify the corresponding heart rate (HR) with a commercial ECG device. Signal processing mechanisms are applied on the raw data to denoise the ECG signal. The captured ECG signal is further processed to extract a breathing pattern to calculate a respiration rate (RR) in breath per minute (BPM). The extracted BPMs are compared with a commercial respiration monitor to validate the data by following a protocol at 5 different BPMs (12, 15, 20, 24 and 30). For each BPM, commercial respiration monitor is validated at first. Then, data are taken simultaneously wearing wrist electrodes and commercial respiratory device to validate the performance of our proposed method at different BPMs. The results indicate high accuracy of the proposed system which is low-cost, simpler to implement, can be integrated with a wearable device and remove the demand of any dedicated sensor for RR measurements.
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Rahman et al. (2021) studied this question. Wrist ECG-derived respiration rate extraction vs. Commercial respiration monitor was evaluated on Accuracy of extracted respiration rate at 5 different BPMs (12, 15, 20, 24, and 30). Extracting respiration rate from wrist ECG signals demonstrated high accuracy compared to a commercial respiration monitor across 5 different breathing rates.
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