Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands.
Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania.
Sensors (Basel). 2023 Mar 31;23(7):3632. doi: 10.3390/s23073632.
The employment of wearable systems for continuous monitoring of vital signs is increasing. However, due to substantial susceptibility of conventional bio-signals recorded by wearable systems to motion artifacts, estimation of the respiratory rate (RR) during physical activities is a challenging task. Alternatively, functional Near-Infrared Spectroscopy (fNIRS) can be used, which has been proven less vulnerable to the subject's movements. This paper proposes a fusion-based method for estimating RR during bicycling from fNIRS signals recorded by a wearable system.
Firstly, five respiratory modulations are extracted, based on amplitude, frequency, and intensity of the oxygenated hemoglobin concentration (O2Hb) signal. Secondly, the dominant frequency of each modulation is computed using the fast Fourier transform. Finally, dominant frequencies of all modulations are fused, based on averaging, to estimate RR. The performance of the proposed method was validated on 22 young healthy subjects, whose respiratory and fNIRS signals were simultaneously recorded during a bicycling task, and compared against a zero delay Fourier domain band-pass filter.
The comparison between results obtained by the proposed method and band-pass filtering indicated the superiority of the former, with a lower mean absolute error (3.66 vs. 11.06 breaths per minute, p<0.05). The proposed fusion strategy also outperformed RR estimations based on the analysis of individual modulation.
This study orients towards the practical limitations of traditional bio-signals for RR estimation during physical activities.
可穿戴系统在连续监测生命体征方面的应用越来越广泛。然而，由于传统可穿戴系统记录的生物信号对运动伪影的高度敏感，在进行体育活动时，估计呼吸率（RR）是一项具有挑战性的任务。相反，可以使用功能近红外光谱（fNIRS），它被证明对受试者的运动不那么敏感。本文提出了一种基于融合的方法，用于从可穿戴系统记录的 fNIRS 信号中估计骑自行车时的 RR。
首先，基于氧合血红蛋白浓度（O2Hb）信号的幅度、频率和强度，提取了五个呼吸调制。其次，使用快速傅里叶变换计算每个调制的主导频率。最后，基于平均融合所有调制的主导频率，以估计 RR。该方法的性能在 22 名年轻健康受试者中进行了验证，他们在骑自行车任务期间同时记录了呼吸和 fNIRS 信号，并与零延迟傅里叶域带通滤波器进行了比较。
与带通滤波相比，所提出方法的结果具有较低的平均绝对误差（3.66 与 11.06 次/分钟，p<0.05），表明前者具有优越性。基于单个调制分析的 RR 估计也优于所提出的融合策略。
本研究针对传统生物信号在体育活动中估计 RR 时的实际局限性。
