Bergmann Tobias, Froese Logan, Gomez Alwyn, Sainbhi Amanjyot Singh, Vakitbilir Nuray, Islam Abrar, Stein Kevin, Marquez Izzy, Amenta Fiorella, Park Kevin, Ibrahim Younis, Zeiler Frederick A
Biosystems Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.
Bioengineering (Basel). 2023 Dec 27;11(1):0. doi: 10.3390/bioengineering11010033.
Regional cerebral oxygen saturation (rSO), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The objective was to evaluate the applicability of using wavelet analysis as an automated method for simple signal loss artifact clearance of rSO signals obtained from commercially available devices. A retrospective observational study using existing populations (healthy control (HC), elective spinal surgery patients (SP), and traumatic brain injury patients (TBI)) was conducted. Arterial blood pressure (ABP) and rSO data were collected in all patients. Wavelet analysis was determined to be successful in removing simple signal loss artifacts using wavelet coefficients and coherence to detect signal loss artifacts in rSO signals. The removal success rates in HC, SP, and TBI populations were 100%, 99.8%, and 99.7%, respectively (though it had limited precision in determining the exact point in time). Thus, wavelet analysis may prove to be useful in a layered approach NIRS signal artifact tool utilizing higher-frequency data; however, future work is needed.
局部脑氧饱和度（rSO）是一种测量脑组织氧合的方法，通过无创近红外光谱（NIRS）设备进行记录。一个主要限制是记录的信号常常包含伪迹。手动去除这些伪迹既耗费资源又耗时。目的是评估使用小波分析作为一种自动方法，用于清除从市售设备获得的rSO信号中简单信号丢失伪迹的适用性。进行了一项回顾性观察研究，使用现有的人群（健康对照（HC）、择期脊柱手术患者（SP）和创伤性脑损伤患者（TBI））。收集了所有患者的动脉血压（ABP）和rSO数据。通过小波系数和相干性来检测rSO信号中的信号丢失伪迹，确定小波分析成功地去除了简单信号丢失伪迹。HC、SP和TBI人群中的去除成功率分别为100%、99.8%和99.7%（尽管在确定确切时间点方面精度有限）。因此，小波分析可能在利用高频数据的分层近红外光谱信号伪迹工具方法中被证明是有用的；然而，还需要未来的工作。
