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. 2024 Apr 16;11(4):385.
doi: 10.3390/bioengineering11040385.

Application of the Single Source-Detector Separation Algorithm in Wearable Neuroimaging Devices: A Step toward Miniaturized Biosensor for Hypoxia Detection

Thien Nguyen  1 , Soongho Park  1 , Jinho Park  1 , Asma Sodager  1 , Tony George  1 , Amir Gandjbakhche  1
Affiliations

Application of the Single Source-Detector Separation Algorithm in Wearable Neuroimaging Devices: A Step toward Miniaturized Biosensor for Hypoxia Detection

Thien Nguyen et al. Bioengineering (Basel). .

Abstract

Most currently available wearable devices to noninvasively detect hypoxia use the spatially resolved spectroscopy (SRS) method to calculate cerebral tissue oxygen saturation (StO2). This study applies the single source-detector separation (SSDS) algorithm to calculate StO2. Near-infrared spectroscopy (NIRS) data were collected from 26 healthy adult volunteers during a breath-holding task using a wearable NIRS device, which included two source-detector separations (SDSs). These data were used to derive oxyhemoglobin (HbO) change and StO2. In the group analysis, both HbO change and StO2 exhibited significant change during a breath-holding task. Specifically, they initially decreased to minimums at around 10 s and then steadily increased to maximums, which were significantly greater than baseline levels, at 25-30 s (p-HbO < 0.001 and p-StO2 < 0.05). However, at an individual level, the SRS method failed to detect changes in cerebral StO2 in response to a short breath-holding task. Furthermore, the SSDS algorithm is more robust than the SRS method in quantifying change in cerebral StO2 in response to a breath-holding task. In conclusion, these findings have demonstrated the potential use of the SSDS algorithm in developing a miniaturized wearable biosensor to monitor cerebral StO2 and detect cerebral hypoxia.

Keywords: breath holding; cerebral tissue oxygen saturation; cerebral tissue oxygenation; near-infrared spectroscopy; spatially resolved spectroscopy.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Illustration of devices that use the SRS method and SSDS algorithm. SRS-based devices need to have at least 2 source—detector pairs, while SSDS-based devices need only one source—detector pair; hence, they can be made smaller and simpler. In addition, there is an assumption that d has to be significantly larger than ∂d in the SRS method. As a result, SRS-based devices need to have detectors far away from the light source, which reduces the signal to noise ratio and increases the device size.
Figure 2
Figure 2
Cerebral HbO changes due to the breath-holding task; (a) HbO change from 3 cm SDS; (b) HbO change from 4 cm SDS. Error bars represent standard error. * indicates a p value < 0.05 and ** indicates a p value < 0.001.
Figure 3
Figure 3
Change in StO2-SRS due to the breath-holding task. Error bars represent standard error. * indicates a p value < 0.05. StO2-SRS change was obtained from the processed data worksheet.
Figure 4
Figure 4
Change in StO2-SSDS due to the breath-holding task. (a) StO2-SSDS calculated from 3 cm SDS; (b) StO2-SSDS calculated from 4 cm SDS. Error bars represent standard error. * indicates a p value < 0.05.
Figure 5
Figure 5
StO2-SRS and StO2-SSDS before, during, and after a breath-holding task; (a) short task and (b) long task. Vertical lines mark the start and end of the breath-holding task.
See this image and copyright information in PMC

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