Reflective oxygen saturation monitoring at hypothenar and its validation by human hypoxia experiment

A comparative study of accuracy in major adaptive filters for motion artifact removal in sleep apnea tests

Sleep apnea is probably the most common respiratory disorder; respiration and blood oxygen saturation (SpO2) are major concerns in sleep apnea and are also the two main parameters checked by polysomnography (PSG, the gold standard for diagnosing sleep apnea). In this study, we used a simple, non-invasive monitoring system based on photoplethysmography (PPG) to continuously monitor SpO2 and heart rate (HR) for individuals at home. Various breathing experiments were conducted to investigate the relationship between SpO2, HR, and apnea under different conditions, where two techniques (empirical formula and customized formula) for calculating SpO2 and two methods (resting HR and instantaneous HR) for assessing HR were compared. Various adaptive filters were implemented to compare the effectiveness in removing motion artifacts (MAs) during the tests. This study fills the gap in the literature by comparing the performance of different adaptive filters on estimating SpO2 and HR during apnea. The results showed that up-down finger motion introduced more MA than left-right motion, and the errors in SpO2 estimation were increased as the frequency of movement was increased; due to the low sampling frequency features of these tests, the insertion of adaptive filter increased the noise in the data instead of eliminating the MA for SpO2 estimation; the normal least mean squares (NLMS) filter is more effective in removing MA in HR estimation than other filters.

Reflection-Boosted Wearable Ring-Type Pulse Oximeters for SpO2 Measurement with High Sensitivity and Low Power Consumption

In this study, we demonstrated a Monte Carlo simulation to model a finger structure and to calculate the intensity of photons passing through tissues, in order to determine optimal angular separation between a photodetector (PD) and a light-emitting diode (LED), to detect SpO₂. Furthermore, our model was used to suggest a mirror-coated ring-type pulse oximeter to improve the sensitivity by up to 80% and improve power consumption by up to 65% compared to the mirror-uncoated structure. A ring-type pulse oximeter (RPO) is widely used to detect photoplethysmography (PPG) signals for SpO₂ measurement during sleep and health-status monitoring. Device sensitivity and the power consumption of an RPO, which are key performance indicators, vary greatly with the geometrical arrangement of PD and LED within the inner surface of an RPO. We propose a reflection-boosted design of an RPO to achieve both high sensitivity and low power consumption, and determine an optimal configuration of a PD and LED by performing a 3D Monte Carlo simulation and confirming its agreement with experimental measurement. In order to confirm the reflection-boosted performance in terms of signal-to-noise ratio, R ratio, and perfusion index (PI), RPOs were fabricated with and without a highly reflective coating, and then used for SpO₂ measurement from eight participants. Our simulation allows the numerical calculation of the intensity of photon passing and scattering through finger tissues. The reflection-boosted RPO enables reliable measurement with high sensitivity, resulting in less power consumption for the LED and longer device usage than conventional RPOs without any reflective coating, in order to maintain the same level of SNR and PI. Compared to the non-reflective reference RPO, the reflection-boosted RPO design greatly enhanced both detected light intensity (67% in dc and 322% in ac signals at a wavelength λ₁ = 660 nm, and also 81% and 375% at λ₂ = 940 nm, respectively) and PI (23.3% at λ₁ and 25.5% at λ₂). Thus, the reflection-boosted design not only enhanced measurement reliability but also significantly improved power consumption, i.e., by requiring only 36% and 30% power to drive the LED sources with λ₁ and λ₂, respectively, to produce the device performance of a non-reflective RPO reference. It is expected that our proposed RPO provides long-term monitoring capability with low power consumption and an enhanced PI for SpO₂ measurement.

Modulation of thalamic network connectivity using transcranial direct current stimulation based on resting-state functional magnetic resonance imaging to improve hypoxia-induced cognitive impairments

Hypoxic conditions at high altitudes severely affect cognitive functions such as vigilance, attention, and memory and reduce cognitive ability. Hence, there is a critical need to investigate methods and associated mechanisms for improving the cognitive ability of workers at high altitudes. This study aimed to use transcranial direct current stimulation (tDCS) to modulate thalamic network functional connectivity to enhance cognitive ability. We recruited 20 healthy participants that underwent hypoxia exposure in a hypoxic chamber at atmospheric pressure to simulate a hypoxic environment at 4,000 m. Participants received both sham and real stimulation. tDCS significantly improved the participants’ emotional status, including depression, fatigue, and energy level. These effects were sustained for more than 6 h (P < 0.05 at the second to fifth measurements). In addition, tDCS enhanced vigilance, but this was only effective within 2 h (P < 0.05 at the second and third measurements). Central fatigue was significantly ameliorated, and cerebral blood oxygen saturation was increased within 4 h (P < 0.05 at the second, third, and fourth measurements). Furthermore, functional connectivity results using the thalamus as a seed revealed enhanced connectivity between the thalamus and hippocampus, cingulate gyrus, and amygdala after tDCS. These results indicated that tDCS increased local cerebral blood oxygen saturation and enhanced thalamic network connectivity in a hypoxic environment, thereby improving vigilance, depression, fatigue, and energy levels. These findings suggest that tDCS may partially rescue the cognitive decline caused by hypoxia within a short period. This approach affords a safe and effective cognitive enhancement method for all types of high-altitude workers with a large mental load.

Accuracy enhancement in reflective pulse oximetry by considering wavelength-dependent pathlengths

Objective. Noninvasive measurement of oxygen saturation (SpO₂) using pulse oximetry based on transmissive photoplethysmography (tPPG) is clinically accepted and widely employed. However, reflective photoplethysmography (rPPG) - present in smartwatches - has not become equally accepted, partially because the pathlengths of the red and infrared PPGs are patient-dependent. Thus, even the most popular "Ratio of Modulation" (R) method requires patient-dependent calibration to reduce the errors in the measurement of SpO2 using rPPGs.Approach. In this paper, a correction factor or "pathlength ratio" β is introduced in an existing calibration-free algorithm that compensates the patient-dependent pathlength variations, and improved accuracy is obtained in the measurement of SpO2 using rPPGs. The proposed β is derived through the analytical model of a rPPG signal. Using the new expression and data obtained from a human hypoxia study wherein arterial oxygen saturation values acquired through Blood Gas Analysis were employed as a reference, β is determined.Main results. The results of the analysis show that a specific combination of the β and the measurements on the pulsating part of the natural logarithm of the red and infrared PPG signals yields a reduced root-mean-square error (RMSE). It is shown that the average RMSE in measuring SpO2 values reduces to 1 %.Significance. The human hypoxia study data used for this work, obtained in a previous study, coversSpO₂values in the range from 70 % to 100 %, and thus shows that the pathlength ratio β proposed here works well in the range of clinical interest. This work demonstrates that the calibration-free method applicable for transmission type PPGs can be extended to determineSpO₂using reflective PPGs with the incorporation of the correction factor β. Our algorithm significantly reduces the number of parameters needed for the estimation, while keeping the RMSE below the clinically accepted 2 %.

Home Respiratory Care: Design of a Prototype for Continuous Measurement at the Nasal Septum

Chronic respiratory diseases have been on the rise, especially due to COVID-19, extreme air pollution, and other external circumstances. Millions of people around the world suffer from progressive lung diseases and require supplemental oxygen therapy to maintain blood oxygen (SpO2) levels above 90% to prevent hypoxic episodes that can lead to further organ damage. Today, these chronic episodes are more prevalent in aging populations suffering from Chronic Obstructive Pulmonary Disorder (COPD). Existing SpO2 measurement equipment, designed to assist with treating COPD at home, are suboptimal as they cannot measure SpO2 levels continuously, meaning supplemental oxygen devices are unable to adjust oxygen flow rates to the patient’s needs. These discrepancies can result in hypoxic episodes of blood oxygen levels below 90%. Following this need, our team demonstrates preliminary results of the novel placement of a SpO2 sensor in the nasal septum to allow for comfortable and sustained SpO2 measurement. This will improve the experience of home-respiratory care with continuously obtained data from a novel location.

Machine Learning based SpO2 Computation Using Reflectance Pulse Oximetry

Continuous monitoring of blood oxygen saturation level (SpO₂) is crucial for patients with cardiac and pulmonary disorders and those undergoing surgeries. SpO₂ monitoring is widely used in a clinical setting to evaluate the effectiveness of lung medication and ventilator support. Owing to its high levels of accuracy and stability, transmittance pulse oximeters are widely used in the clinical community to compute SpO₂. Transmittance pulse oximeters are limited to measure SpO₂ only from peripheral sites. Reflectance pulse oximeters, however, can be used at various measurement sites like finger, wrist, chest, forehead, and are immune to faulty measurements due to vasoconstriction and perfusion changes. Reflectance pulse oximeters are not widely adopted in clinical environments due to faulty measurements and inaccurate R-value based calibration methods. In this paper, we present the analysis and observations made using a machine learning model for SpO₂ computation using reflectance Photoplethysmogram (PPG) signals acquired from the finger using the custom data acquisition platform. The proposed model overcomes the limitations imposed by the traditional R-value based calibration method through the use of a machine learning model using various time and frequency domain features. The model was trained and tested using the clinical data collected from 95 subjects with SpO₂ levels varying from 81-100% using the custom SpO₂ data acquisition platform along with reference measures. The proposed model has an absolute mean error of 0.5% with an accuracy of 96 ± 2% error band for SpO₂ values ranging from 81-100%.