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Smartphone-Based SpO 2 Measurement by Exploiting Wavelengths Separation and Chromophore Compensation
Abstract
Patients with respiratory diseases require frequent and accurate blood oxygen level monitoring. Existing techniques, however, either need a dedicated hardware or fail to predict low saturation levels. To fill in this gap, we propose a phone-based oxygen level estimation system, called PhO 2 , using camera and flashlight functions that are readily available on today’s off-the-shelf smartphones. Since the phone’s camera and flashlight were not made for this purpose, utilizing them for oxygen level estimation poses many difficulties. We introduce a cost-effective add-on together with a set of algorithms for spatial and spectral optical signal modulation to amplify the optical signal of interest while minimizing noise. A near-field-based pressure detection and feedback mechanism are also proposed to mitigate the negative impacts of user’s behavior during the measurement. We also derive a non-linear referencing model with an outlier removal technique that allows PhO 2 to accurately estimate the oxygen level from color intensity ratios produced by the smartphone’s camera.
An evaluation on COTS smartphone with six subjects shows that PhO 2 can estimate the oxygen saturation within 3.5% error rate comparing to FDA-approved gold standard pulse oximetry. In addition, our evaluation in hospitals presents high correlation with ground-truth qualified by the 0.83/1.0 Kendall τ coefficient.
... The unwanted low frequencies were removed by application of a Gaussian filter with a width of 1 s. Bui et al. [14] recently introduced add-on hardware for the smartphone camera with optical filters, which can isolate specific wavelength bands (green band and red band) emitted by the LED flashlight. In addition, instead of using a traditional linear regression model for the correlation of the SpO 2 with the AC/DC signal ratio, an alternative algorithm was used. ...
... However, the total training time for application of the CNN algorithm was extremely long, rendering the method impractical. The accuracy and repeatability of the measurements are affected by many factors, including the size of the finger and its position over the camera, the stability of the finger during the video-recording process and the illumination level [14,15]. The latter depends on the ambient light as well as the battery status, which might affect the intensity of the emitted light from the LED during the recording. ...
... The blue component of the signal, as is illustrated in the Results section, was excluded from our study because of its low correlation with the heartbeat pulse rate. The oxygen concentration in blood can, in principle, be calculated in a non-invasive way using the photoplethysmography technique PPG [6,14], which is based on the differential absorption of light of two different wavelengths from oxygenated and de-oxygenated arterial blood. ...
Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky–Golay filter. Two approaches—gradient and local maximum methods—were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal’s frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated.
... Monitoring blood-oxygen saturation (SpO 2 ) with a smartphone, if enabled in an accurate and unobtrusive manner, has the potential to improve health outcomes for those with respiratory illnesses by enabling access to rapid risk assessment outside of face-to-face clinical settings (1). Recent work on smartphone-based SpO 2 monitors show that these devices may offer the ubiquity and precision necessary to increase access to detection and monitoring of respiratory diseases (2,3). This work builds upon these prior findings by being the first to systematically compare smartphone-based SpO 2 monitoring to standalone pulse oximeters on a wide range of clinically-relevant SpO 2 values (70% ≤ SpO 2 < 100%). ...
... Pulse oximeters typically perform oxygenation measurement via transmittance photoplethysmography (PPG) sensing at the finger tip, clamping around the end of the finger and transmitting red and IR light via LEDs (12). By measuring the resultant ratio of light transmittance, the devices estimate the absorption properties of the blood, using calibrated curves based on the Beer-Lambert Law to infer blood composition (2). While purpose-built pulse oximetry is noninvasive and accurate across a full range of clinically relevant SpO 2 levels and skin tones, it requires a standalone device. ...
... Smartphone-based solutions for monitoring blood oxygen saturation have been explored, employing various solutions used to gather and stabilize the PPG signal, augment the IR-filtered broad-band camera sensor, and filter the resultant signal for noise or outlier correction. Some solutions require extra hardware, such as a color filter or external light source (2,(24)(25)(26)(27)(28), whereas others rely only on the in-built smartphone hardware and employ software techniques to process the PPG signal (3,(29)(30)(31). Various statistical methods have been used to interpret the results to achieve reasonable accuracy, including the ratio-of-ratios method used by standalone pulse oximeters (2) and deep learning (3). ...
Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health, as well as improve physicians' ability to remotely diagnose and treat respiratory conditions. In this work, we take a step towards this goal by performing the first clinical development validation on a smartphone-based SpO sensing system using a varied fraction of inspired oxygen (FiO) protocol, creating a clinically relevant validation dataset for solely smartphone-based methods on a wide range of SpO values (70%-100%) for the first time. This contrasts with previous studies, which evaluated performance on a far smaller range (85%-100%). We build a deep learning model using this data to demonstrate accurate reporting of SpO level with an overall MAE=5.00% SpO and identifying positive cases of low SpO<90% with 81% sensitivity and 79% specificity. We ground our analysis with a summary of recent literature in smartphone-based SpO2 monitoring, and we provide the data from the FiO study in open-source format, so that others may build on this work.
... Vitals Measured Dataset Methodology [13] PR, SpO2, BP private Peaks detection [14] SpO2 private SVD + CNN [15] SpO2 private - [16] PR BUT PPG - [17] PR, PRV Welltory Wavelet Analysis [18] HR, SpO2 MTHS CNN [19] HR, HRV, RR, SpO2 In contrast, this study aims to estimate not only the three vitals (pulse rate, oxygen saturation, and respiratory rate) but also the single-lead electrocardiogram (ECG), which is not addressed in the existing works Pulse Rate: The estimation of pulse rate has traditionally relied on specialized sensors that record PPG signals. However, later studies have explored the potential of mobile phones to record vPPG signals 3 and estimate vital signs. ...
... One of the earliest works utilizing the RoR method for mobile-based SpO2 estimation is [36]; however, its accuracy is not up to the mark based on the FDA clearance threshold as it is also not able to estimate low SpO2 levels. For the patients of respiratory disease, a SpO2 estimation algorithm for low SpO2 level detection is presented in [15]. In [37] RoR and linear regression method is used to measure SpO2. ...
In the post-covid19 era, every new wave of the pandemic causes an increased concern among the masses to learn more about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasive tools for rapid and continuous monitoring of body vitals that reflect the status of one's overall health. In this backdrop, this work proposes a deep learning approach to turn a smartphone-the popular hand-held personal gadget-into a diagnostic tool to measure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), and respiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG) of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip by placing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filtered and/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks (CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise, the contribution of this paper is two-fold: 1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, vision transformer, and most importantly by CLIP model; 2) a novel discrete cosine transform+feedforward neural network-based method that translates the recorded video- PPG signal to a single-lead ECG signal. The proposed method is anticipated to find its application in several use-case scenarios, e.g., remote healthcare, mobile health, fitness, sports, etc.
... From a technological perspective the smartphone camera can be utilized as a sensing device. More particularly, the camera may act as a light spectrum analyzer to measure for example environmental conditions and chemicals [1,21] and to screen for health issues [9,58,60,81,82]. The low cost of this sensing is often highlighted in research. ...
... In this research, the process of taking the picture and the bodily and material alignments this required, was seen as equally important as the produced photograph depicting certain qualities of the body, thus contrasting more medically oriented research (e.g. [9,16,39,58,60,81,82]). We investigate if the smartphone camera could create a more intimate connection to nature in a similar way. ...
Research on environmental sustainability in HCI is investigating the opportunities and hindrances technologies pose on living sustainably, beyond direct material impact of production, use and disposal. With this background, we focus on the smartphone camera as a tool that allows users to quickly and relatively effortlessly depict, save, share, access, augment or amplify information about the environment. Based on two years of participant observation studies, we present examples of how urban farmers use the smartphone camera as a tool in their practice. We discuss how the smartphone camera mediates human experiences of the environment and how certain uses of the camera may contribute to environmental sustainability. We highlight how the smartphone camera used as a tool in gardening was experienced to support (a) feelings of closeness or bonds towards the local environment and (b) the creation and sharing of knowledge
... In addition, it may cause skin damage in subjects with a very fragile skin, especially infants in neonatal care, if it is left attached to the skin for long periods [6]. Moreover, pulse oximetry devices do not always fit well with people's fingertips, depending on patient's age and fingers' size, which may lead to distorted readings [7]. In addition, direct contact with the device exposes people to the risk of infection and skin irritation [8,9]. ...
Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photople-thysmography (iPPG) signal extracted from the human's forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position.
... lamonaca,2015] provided useful insights on processing of change of light intensity in the video frames at the fingertips of the patient.kanva, A.k., Sharma, C.J., & deb, S. (2014) in [kanva,2014], narrowed down on the extraction of data of recorded variations in colour signals on a fingertip placed in contact with the optical sensor and Bui, nguyen, nguyen et al.(2020) in [nam Bui,2020] gave systematic insights about measuring SpO2 by exploiting wavelength separation and chromophore compensation. The motivation and feasibility for development of the bluetooth proximity sensing module and its importance to return to normalcy in times of pandemic was conveyed by xia, ye and lee(2020) in [xia,2020]. ...
Objectives: Photoplethysmogram (PPG) signals have become a crucial tool in the non-invasive monitoring of oxygen saturation levels (SpO2). The main purpose of the present review is to perform a meta-analysis of the involvement and consideration of critical SpO2 levels (<90%) in the research papers where SpO2 levels are calculated/ predicted from PPG and to elaborate on the impact of the critical levels when presenting the evaluation results. Data sources: PubMed, Science Direct, and Scopus were searched for papers published between January 1, 2016, and September 10, 2022. Results: This study produced several results, concerning the main objective as well as other important issues for improving the SpO2 estimation/calculation. We discovered that only 21 out of 75 papers considered SpO2 values that are in the critical domain. Many papers do not provide access to their databases or disclose the software/models used. Additionally, some studies lack sufficient testing subjects and fail to make their results reproducible. The findings reveal a preference for SpO2 calculation over prediction, limited data availability , undisclosed methodologies, and diverse evaluation metrics hinder replication and direct comparisons between studies. Also, a scoring table is offered that scores higher the papers that are more valuable for SpO2 calculation/ prediction.Conclusion: Employing PRISMA guidelines, a comprehensive search across PubMed, Science Direct, and Scopus databases initially extracted 6173 potential papers. Following rigorous screening, 75 papers were selected for detailed analysis, of which only 21 included data from critical SpO2 levels. Furthermore, this research provided information for the filtered 21 paper about the sample size of the study participants, the models utilized to derive the results, the availability of databases, the specific devices employed in the research, the methodologies employed for PPG signal measurement, and the collaborative efforts among authors from different institutions. This information is sublimed in the scoring table which gives higher scoring to those papers that are more valuable for SpO2 calculation/prediction. This study offers references to all these findings that can be used as concrete guidelines for prospective researchers and developers of new sensors for SpO2 estimation/calculation utilizing PPG signals. ABBREVIATIONS PPG = Photoplethysmogram SpO2 = Blood oxygen saturation RMSE = Root-mean-square error MAE = Mean absolute error RMSPE = Root-mean-square percentage error TRE = Total relative error AMAE = Average mean absolute error AMSE = Average mean squared error RMSEP = Root mean square error of prediction. STRENGTHS AND LIMITATIONS OF THIS STUDY • This is the first systematic review and meta-analysis to evaluate the inclusion of critical oxygen saturation levels when estimated from PPG. • Several other factors are assessed in studies examining critical SpO2 levels, including the number of subjects involved, the types of models used, database availability, device types, methods of PPG signal measurement, and collaboration between different institutions. • The main limitation of this research is the high level of heterogeneity among the studies in presenting their results, which poses challenges in assessing their quality regarding the interest of this research.
Hyperspectral imaging captures scene information across narrow, contiguous bands of the electromagnetic spectrum. Despite its proven utility in industrial and biomedical applications, its ubiquity has been limited by bulky form factors, slow capture times, and prohibitive costs. In this work, we propose a generalized approach to snapshot hyperspectral imaging that only requires a standard rolling shutter camera and wavelength-adjustable lighting. The crux of this approach entails using the rolling shutter as a spatiotemporal mask, varying incoming light quicker than the camera's frame rate in order for the captured image to contain rows of pixels illuminated at different wavelengths. An image reconstruction pipeline then converts this coded image into a complete hyperspectral image using sparse optimization. We demonstrate the feasibility of this approach by deploying a low-cost system called ChromaFlash, which uses a smartphone's camera for image acquisition and a series of LEDs to change the scene's illumination. We evaluated ChromaFlash through simulations on two public hyperspectral datasets and assessed its spatial and spectral accuracy across various system parameters. We also tested the real-world performance of our prototype by capturing diverse scenes under varied ambient lighting conditions. In both experiments, ChromaFlash outperformed state-of-the-art techniques that use deep learning to convert RGB images into hyperspectral ones, achieving snapshot performance not demonstrated by prior attempts at accessible hyperspectral imaging.
Significance
Monitoring oxygen saturation (SpO2) is important in healthcare, especially for diagnosing and managing pulmonary diseases. Non-contact approaches broaden the potential applications of SpO2 measurement by better hygiene, comfort, and capability for long-term monitoring. However, existing studies often encounter challenges such as lower signal-to-noise ratios and stringent environmental conditions.
Aim
We aim to develop and validate a contactless SpO2 measurement approach using 3D convolutional neural networks (3D CNN) and 3D visible-near-infrared (VIS-NIR) multimodal imaging, to offer a convenient, accurate, and robust alternative for SpO2 monitoring.
Approach
We propose an approach that utilizes a 3D VIS-NIR multimodal camera system to capture facial videos, in which SpO2 is estimated through 3D CNN by simultaneously extracting spatial and temporal features. Our approach includes registration of multimodal images, tracking of the 3D region of interest, spatial and temporal preprocessing, and 3D CNN-based feature extraction and SpO2 regression.
Results
In a breath-holding experiment involving 23 healthy participants, we obtained multimodal video data with reference SpO2 values ranging from 80% to 99% measured by pulse oximeter on the fingertip. The approach achieved a mean absolute error (MAE) of 2.31% and a Pearson correlation coefficient of 0.64 in the experiment, demonstrating good agreement with traditional pulse oximetry. The discrepancy of estimated SpO2 values was within 3% of the reference SpO2 for ∼80% of all 1-s time points. Besides, in clinical trials involving patients with sleep apnea syndrome, our approach demonstrated robust performance, with an MAE of less than 2% in SpO2 estimations compared to gold-standard polysomnography.
Conclusions
The proposed approach offers a promising alternative for non-contact oxygen saturation measurement with good sensitivity to desaturation, showing potential for applications in clinical settings.
Smartphone camera photoplethysmography (cPPG) enables non-invasive pulse oximetry and hemoglobin concentration measurements. However, the aesthetic-driven non-linearity in default image capture and preprocessing pipelines poses challenges for consistency and transferability of cPPG across devices. This work identifies two key parameters—tone mapping and sensor threshold—that significantly impact cPPG measurements. We propose a novel calibration method to linearize camera measurements, thus enhancing consistency and transferability of cPPG across devices. A benchtop calibration system is also presented, leveraging a microcontroller and LED setup to characterize these parameters for each phone model. Our validation studies demonstrate that, with appropriate calibration and camera settings, cPPG applications can achieve 74% higher accuracy than with default settings. Moreover, our calibration method proves effective across different smartphone models ( N = 4 ), and calibrations performed on one phone can be applied to other smartphones of the same model ( N = 6 ), enhancing consistency and scalability of cPPG applications.
In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learn more about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasive tools for rapid and continuous monitoring of body vitals that reflect the status of one’s overall health. In this backdrop, this work proposes a deep learning approach to turn a smartphone—the popular hand-held personal gadget—into a diagnostic tool to measure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), and respiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG) of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip by placing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filtered and/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks (CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise, the contribution of this paper is twofold: (1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); (2) a novel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to a single-lead ECG signal. The significance of this work is twofold: (i) it allows rapid self-testing of body vitals (e.g., self-monitoring for covid19 symptoms), (ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrial fibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases, and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead to reduction in health insurance costs.
Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO 2 ) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO 2 sensing system using a varied fraction of inspired oxygen (FiO 2 ) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO 2 values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO 2 while identifying positive cases of low SpO 2 < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work.
Blood oxygen saturation (SpO2) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO2. Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO2 monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO2 information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO2 prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
Blood oxygen saturation (SpO
) is an important indicator forpulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO
. Most of these works are contact-based, requiring users to cover a phone’s camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO
monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO
information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO
prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
Sensors play an important role in measuring biosignals. These sensors evolved from analog form to current digital avatar. First formally known cardiovascular sensing device is a stethoscope. It has evolved from its earliest version of a wooden tube of specific dimensions to binaural stethoscope. to a digital stethoscope in which the sound of heart pulses can be digitized, annotated, and processed. ECGs have also evolved from room-size machines to small credit-card-sized wearable devices. This chapter will describe the basic building blocks of such many cardiovascular sensing devices, how they can be used to predict cardiovascular issues, and a point of view on how they will evolve further.
A biomaterial is basically a substance, whether natural or manmade, that constitutes an entire or a portion of a living structure or biomedical tool that executes, performs and enhances a natural feature, which can be used for a medical application and modified for it. Biomaterials have a benign role, including to be used for a heart regulator, which can be bioactive, similar to hydroxyapatite covered hip implants, designed for a much more active purpose. In surgery, dental applications and medicine delivery, biomaterials are still being used every day (Isa et al. 2000). The synthetic or natural materials such as ceramic, metals, polymerics and their composite materials can efficiently integrate into the body and are known as biomaterials, being biocompatible with living tissues (Ozkizilcik and Tuzlakoglu 2014). Furthermore, biomaterials are bioactive materials, while the biomimetic materials and biodegradable materials are biologically inactive materials (Li et al. 2007). The materials which interact with body parts and body fluids are important in hand bones, tendons, nerves, heart valves, blood vessel prosthesis and cochlear replacements (Cicco et al. 2015).
Polymers-based biomaterials have been broadly used as constituents of medical devices, and miniature drug delivery carriers and have significant impact in the advancement of modern medicine. Functional polymers are the building blocks to design many complex structures such as bioconjugation, biocompatible surfaces, bioactive polymer scaffolds, etc. Many artificial human organs can be created from these sustainable polymeric composite materials. The essential material properties required for biomedical applications are super hydrophobicity, adhesive and self-healing. Furthermore, polymer-coated or functionalized nanoparticles are also used for biomedical applications. This chapter discuss the progress in various classes of polymer-based nanostructure materials and composites that have been investigated for biomedical applications. The chapter also inspects the scientific challenges in the recent research and the development of polymer nanocomposites and tuning the properties, for their contribution in the biomedical industry. It also explores the importance of nanostructured polymer composites in the applications such as artificial bone and skin, tissue engineering, drug delivery, etc.
3D printing (3DP) is an emerging technology that has vast potential in the medicinal field to develop complex 3D structures and manufacture complicated 3-dimensional device structures. It has been started as an assessment with the developing tissues, organs, and cellular structure in the medical industry by means of 3D layer by layer (bottom to top) printing. 3D printing technology allocates the rapid development of tailored prosthetics and implants with complicated structural designs. This chapter highlights the polymer material properties for 3D printing, also referred as additive manufacturing (AM) and discusses the advantages of AM over conventional manufacturing processes, as AM has the capacity to fabricate an extensive range of bioinspired structures. Its key advantage on production of composites includes rapid, high precision and customized geometry. This chapter also summarizes the advancement in the 3DP technology and several existing techniques, for developing polymer-based components with multifunctional properties specifically employed in the biomedical device fabrication.
Background
Many commodity pulse oximeters are insufficiently calibrated for patients with darker skin. We demonstrate a quantitative measurement of this disparity in peripheral blood oxygen saturation (SpO2) with a controlled experiment. To mitigate this, we present OptoBeat, an ultra–low-cost smartphone-based optical sensing system that captures SpO2 and heart rate while calibrating for differences in skin tone. Our sensing system can be constructed from commodity components and 3D-printed clips for approximately US $1. In our experiments, we demonstrate the efficacy of the OptoBeat system, which can measure SpO2 within 1% of the ground truth in levels as low as 75%.
Objective
The objective of this work is to test the following hypotheses and implement an ultra–low-cost smartphone adapter to measure SpO2: skin tone has a significant effect on pulse oximeter measurements (hypothesis 1), images of skin tone can be used to calibrate pulse oximeter error (hypothesis 2), and SpO2 can be measured with a smartphone camera using the screen as a light source (hypothesis 3).
Methods
Synthetic skin with the same optical properties as human skin was used in ex vivo experiments. A skin tone scale was placed in images for calibration and ground truth. To achieve a wide range of SpO2 for measurement, we reoxygenated sheep blood and pumped it through synthetic arteries. A custom optical system was connected from the smartphone screen (flashing red and blue) to the analyte and into the phone’s camera for measurement.
Results
The 3 skin tones were accurately classified according to the Fitzpatrick scale as types 2, 3, and 5. Classification was performed using the Euclidean distance between the measured red, green, and blue values. Traditional pulse oximeter measurements (n=2000) showed significant differences between skin tones in both alternating current and direct current measurements using ANOVA (direct current: F2,5997=3.1170 × 105, P<.01; alternating current: F2,5997=8.07 × 106, P<.01). Continuous SpO2 measurements (n=400; 10-second samples, 67 minutes total) from 95% to 75% were captured using OptoBeat in an ex vivo experiment. The accuracy was measured to be within 1% of the ground truth via quadratic support vector machine regression and 10-fold cross-validation (R2=0.97, root mean square error=0.7, mean square error=0.49, and mean absolute error=0.5). In the human-participant proof-of-concept experiment (N=3; samples=3 × N, duration=20-30 seconds per sample), SpO2 measurements were accurate to within 0.5% of the ground truth, and pulse rate measurements were accurate to within 1.7% of the ground truth.
Conclusions
In this work, we demonstrate that skin tone has a significant effect on SpO2 measurements and the design and evaluation of OptoBeat. The ultra-low-cost OptoBeat system enables smartphones to classify skin tone for calibration, reliably measure SpO2 as low as 75%, and normalize to avoid skin tone–based bias.
Blood oxygen saturation (SpO2) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO2. Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO2 monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO2 information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO2 prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
Accurately measuring and monitoring patient's blood oxygen level plays a critical role in today's clinical diagnosis and healthcare practices. Existing techniques however either require a dedicated hardware or produce inaccurate measurements. To fill in this gap, we propose a phone-based oxygen level estimation system, called PhO2, using camera and flashlight functions that are readily available on today's off-the-shelf smart phones. Since phone's camera and flashlight are not made for this purpose, utilizing them for oxygen level estimation poses many challenges. We introduce a cost-effective add-on together with a set of algorithms for spatial and spectral optical signal modulation to amplify the optical signal of interest while minimizing noise. A light-based pressure detection algorithm and feedback mechanism are also proposed to mitigate the negative impacts of user's behavior during the measurement. We also derive a non-linear referencing model that allows PhO2 to estimate the oxygen level from color intensity ratios produced by smartphone's camera.
An evaluation using a custom-built optical element on COTS smartphone with 6 subjects shows that PhO2 can estimate the oxygen saturation within 3.5% error rate comparing to FDA-approved gold standard pulse oximetry. A user study to gauge the reception of PhO2 shows that users are comfortable self-operating the device, and willing to carry the device when going out.
This paper introduces LIBS, a light-weight and inexpensive wearable sensing system, that can capture electrical activities of human brain, eyes, and facial muscles with two pairs of custom-built flexible electrodes each of which is embedded on an off-the-shelf foam earplug. A supervised non-negative matrix factorization algorithm to adaptively analyze and extract these bioelectrical signals from a single mixed in-ear channel collected by the sensor is also proposed. While LIBS can enable a wide class of low-cost self-care, human computer interaction, and health monitoring applications, we demonstrate its medical potential by developing an autonomous whole-night sleep staging system utilizing LIBS's outputs. We constructed a hardware prototype from off-the-shelf electronic components and used it to conduct 38 hours of sleep studies on 8 participants over a period of 30 days. Our evaluation results show that LIBS can monitor biosignals representing brain activities, eye movements, and muscle contractions with excellent fidelity such that it can be used for sleep stage classification with an average of more than 95% accuracy.
The intelligent wearable heart rate measurement requirement has attracted more and more attention, and the related applications of Internet of Things are emerging. However, under intensive physical exercises, motion artifacts are strong interference sources for wrist-type photoplethysmography (PPG) sensor signals, thus significantly affecting the accurate estimation of heart rate and other physiological parameters. Currently, how to effectively remove the motion artifacts from PPG sensor signals is becoming an active and challenging research realm. In this paper, we propose a multi-channel spectral matrix decomposition (MC-SMD) model to accurately estimate heart rate in the presence of intensive physical activities. Motivated by the observation that the PPG signal spectrum and the acceleration spectrum have almost the same spectral peak positions in the frequency domain, we first model the removal of motion artifacts as a spectral matrix decomposition optimization problem. After removing motion artifacts, we propose a new spectral peak tracking method for estimating heart rate. Experimental results on the well-known PPG data sets recorded from 12 subjects during intensive movements demonstrate that MC-SMD can efficiently remove the motion artifacts and retrieve an accurate heart rate using multi-channel PPG sensor signals.
The pulse oximeter is a popular instrument to monitor the arterial oxygen saturation (SPO2). Although a fingertip-type pulse oximeter is the mainstream one on the market at present, it is still inconvenient for long-term monitoring, in particular, with respect to motion. Therefore, the development of a wearable pulse oximeter, such as a finger base-type pulse oximeter, can effectively solve the above issue. However, the tissue structure of the finger base is complex, and there is lack of detailed information on the effect of the light source and detector placement on measuring SPO2. In this study, the practicability of a ring-type pulse oximeter with a multi-detector was investigated by optical human tissue simulation. The optimal design of a ring-type pulse oximeter that can provide the best efficiency of measuring SPO2 was discussed. The efficiency of ring-type pulse oximeters with a single detector and a multi-detector was also discussed. Finally, a wearable and wireless ring-type pulse oximeter was also implemented to validate the simulation results and was compared with the commercial fingertip-type pulse oximeter.
Oxygen saturation in the arterial blood (SaO2) provides information on the adequacy of respiratory function. SaO2 can be assessed noninvasively by pulse oximetry, which is based on photoplethysmographic pulses in two wavelengths, generally in the red and infrared regions. The calibration of the measured photoplethysmographic signals is performed empirically for each type of commercial pulse-oximeter sensor, utilizing in vitro measurement of SaO2 in extracted arterial blood by means of co-oximetry. Due to the discrepancy between the measurement of SaO2 by pulse oximetry and the invasive technique, the former is denoted as SpO2. Manufacturers of pulse oximeters generally claim an accuracy of 2%, evaluated by the standard deviation (SD) of the differences between SpO2 and SaO2, measured simultaneously in healthy subjects. However, an SD of 2% reflects an expected error of 4% (two SDs) or more in 5% of the examinations, which is in accordance with an error of 3%–4%, reported in clinical studies. This level of accuracy is sufficient for the detection of a significant decline in respiratory function in patients, and pulse oximetry has been accepted as a reliable technique for that purpose. The accuracy of SpO2 measurement is insufficient in several situations, such as critically ill patients receiving supplemental oxygen, and can be hazardous if it leads to elevated values of oxygen partial pressure in blood. In particular, preterm newborns are vulnerable to retinopathy of prematurity induced by high oxygen concentration in the blood. The low accuracy of SpO2 measurement in critically ill patients and newborns can be attributed to the empirical calibration process, which is performed on healthy volunteers. Other limitations of pulse oximetry include the presence of dyshemoglobins, which has been addressed by multiwavelength pulse oximetry, as well as low perfusion and motion artifacts that are partially rectified by sophisticated algorithms and also by reflection pulse oximetry.
Infectious diseases such as pneumonia take the lives of millions of children in low- and middle-income countries every year. Many of these deaths could be prevented with the availability of robust and low-cost diagnostic tools using integrated sensor technology. Pulse oximetry in particular, offers a unique non-invasive and specific test for an increase in the severity of many infectious diseases such as pneumonia. If pulse oximetry could be delivered on widely available mobile phones, it could become a compelling solution to global health challenges. Many lives could be saved if this technology was disseminated effectively in the affected regions of the world to rescue patients from the fatal consequences of these infectious diseases. We describe the implementation of such an oximeter that interfaces a conventional clinical oximeter finger sensor with a smartphone through the headset jack audio interface, and present a simulator-based systematic verification system to be used for automated validation of the sensor interface on different smartphones and media players. An excellent agreement was found between the simulator and the audio oximeter for both oxygen saturation and heart rate over a wide range of optical transmission levels on 4th and 5th generations of the iPod TouchTM and iPhoneTM devices.
The World Health Organization (WHO) recommends using age-specific respiratory rates for diagnosing pneumonia in children. Past studies have evaluated the WHO criteria with mixed results.
We examined the accuracy of clinical and laboratory factors for diagnosing pediatric pneumonia in resource-limited settings.
We conducted a retrospective chart review of children under 5 years of age presenting with respiratory complaints to three rural hospitals in Rwanda who had received a chest radiograph. Data were collected on the presence or absence of 31 historical, clinical, and laboratory signs. Chest radiographs were interpreted by pediatric radiologists as the gold standard for diagnosing pneumonia. Overall correlation and test characteristics were calculated for each categorical variable as compared to the gold standard. For continuous variables, we created receiver operating characteristic (ROC) curves to determine their accuracy for predicting pneumonia.
Between May 2011 and April 2012, data were collected from 147 charts of children with respiratory complaints. Approximately 58% of our sample had radiologist-diagnosed pneumonia. Of the categorical variables, a negative blood smear for malaria (χ(2) = 6.21, p = 0.013) and the absence of history of asthma (χ(2) = 4.48, p = 0.034) were statistically associated with pneumonia. Of the continuous variables, only oxygen saturation had a statistically significant area under the ROC curve (AUC) of 0.675 (95% confidence interval [CI] 0.581-0.769 and p = 0.001). Respiratory rate had an AUC of 0.528 (95% CI 0.428-0.627 and p = 0.588).
Oxygen saturation was the best clinical predictor for pediatric pneumonia and should be further studied in a prospective sample of children with respiratory symptoms in a resource-limited setting.
The Levenberg-Marquardt (LM) algorithm is an iterative technique that locates the minimum of a function that is expressed as the sum of squares of nonlinear functions. It has become a standard technique for nonlinear least-squares problems and can be thought of as a combination of steepest descent and the Gauss-Newton method. This document briefly describes the mathematics behind levmar, a free LM C/C++ implementation that can be found at http://www.ics.forth.gr/˜lourakis/levmar.
Objective:
To establish simultaneous pre- and postductal oxygen saturation nomograms in asymptomatic newborns when screening for critical congenital heart disease (CCHD) at ∼24 hours after birth.
Methods:
Asymptomatic term and late preterm newborns admitted to the newborn nursery were screened with simultaneous pre- and postductal oxygen saturation measurements at ∼24 hours after birth. The screening program was implemented in a stepwise fashion in 3 different affiliated institutions. Data were collected prospectively from July 2009 to March 2012 in all 3 centers.
Results:
We screened 13 714 healthy newborns at a median age of 25 hours. The mean preductal saturation was 98.29% (95% confidence interval [CI]: 98.27-98.31), median 98%, and mean postductal saturation was 98.57% (95% CI: 98.55-98.60), median 99%. The mean difference between the pre- and postductal saturation was -0.29% (95% CI: -0.31 to -0.27) with P < .00005. Its clinical relevance to CCHD screening remains to be determined. The postductal saturation was equal to preductal saturation in 38% and greater than preductal saturation in 40% of the screens.
Conclusions:
We have established simultaneous pre- and postductal oxygen saturation nomograms at ∼24 hours after birth based on >13 000 asymptomatic newborns. Such nomograms are important to optimize screening thresholds and methodology for detecting CCHD.
The use of mobile consumer devices as medical diagnostic tools allows standard medical tests to be performed anywhere. Cameras embedded in consumer devices have previously been used as pulse oximetry sensors. However, technical limitations and implementation challenges have not been described. This manuscript provides a critical analysis of pulse oximeter technology and technical limitations of cameras that can potentially impact implementation of pulse oximetry in mobile phones. Theoretical and practical examples illustrate difficulties and recommendations to overcome these challenges.
The introduction of pulse oximetry in clinical practice has allowed for simple, noninvasive, and reasonably accurate estimation of arterial oxygen saturation. Pulse oximetry is routinely used in the emergency department, the pediatric ward, and in pediatric intensive and perioperative care. However, clinically relevant principles and inherent limitations of the method are not always well understood by health care professionals caring for children. The calculation of the percentage of arterial oxyhemoglobin is based on the distinct characteristics of light absorption in the red and infrared spectra by oxygenated versus deoxygenated hemoglobin and takes advantage of the variation in light absorption caused by the pulsatility of arterial blood. Computation of oxygen saturation is achieved with the use of calibration algorithms. Safe use of pulse oximetry requires knowledge of its limitations, which include motion artifacts, poor perfusion at the site of measurement, irregular rhythms, ambient light or electromagnetic interference, skin pigmentation, nail polish, calibration assumptions, probe positioning, time lag in detecting hypoxic events, venous pulsation, intravenous dyes, and presence of abnormal hemoglobin molecules. In this review we describe the physiologic principles and limitations of pulse oximetry, discuss normal values, and highlight its importance in common pediatric diseases, in which the principle mechanism of hypoxemia is ventilation/perfusion mismatch (eg, asthma exacerbation, acute bronchiolitis, pneumonia) versus hypoventilation (eg, laryngotracheitis, vocal cord dysfunction, foreign-body aspiration in the larynx or trachea). Additional technologic advancements in pulse oximetry and its incorporation into evidence-based clinical algorithms will improve the efficiency of the method in daily pediatric practice.
We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.
During conditions of poor perfusion, the accuracy of conventional extremity-based pulse oximeters may be limited. Limited evidence suggests that forehead perfusion may be better preserved during such periods, but pediatric experience with newer forehead reflectance sensors is limited. We prospectively compared the accuracy of a forehead reflectance sensor, the Max-Fast, with a new-generation digit sensor in pediatric patients.
Pediatric patients > 10 kg and who had arterial catheters were eligible for enrollment. Blood oxygen saturation was simultaneously measured with forehead and digit sensors, and compared to corresponding CO-oximetry-measured arterial oxygen saturation values (S(aO2)) taken at the same times. We used Bland-Altman analysis to calculate the bias and precision of the forehead sensor and the digit sensor relative to the S(aO2) values.
We obtained 116 sample sets from 28 patients. The S(aO2) values ranged from 84.1% to 99.2%. The bias and precision of the forehead-to-S(aO2) difference were 0.6% and 2.7%, respectively, versus 1.4% and 2.6%, respectively, for the digit-to-S(aO2) difference (p < 0.05). Bias and precision were 0.7% and 2.6% versus 1.7% and 2.3% for the forehead and digit sensors, respectively, (p < 0.05) in patients who received vasoactive medications, compared with 0.5% and 2.8% versus 1.1% and 2.8% (p = not significant), respectively, in patients who did not receive vasoactive medications.
The Max-Fast sensor estimated S(aO2) as accurately as did a new-generation digit sensor in well-perfused pediatric patients.
Near-infrared spectroscopy or imaging has been extensively applied to various biomedical applications since it can detect the concentrations of oxyhaemoglobin (HbO(2)), deoxyhaemoglobin (Hb) and total haemoglobin (Hb(total)) from deep tissues. To quantify concentrations of these haemoglobin derivatives, the extinction coefficient values of HbO(2) and Hb have to be employed. However, it was not well recognized among researchers that small differences in extinction coefficients could cause significant errors in quantifying the concentrations of haemoglobin derivatives. In this study, we derived equations to estimate errors of haemoglobin derivatives caused by the variation of haemoglobin extinction coefficients. To prove our error analysis, we performed experiments using liquid-tissue phantoms containing 1% Intralipid in a phosphate-buffered saline solution. The gas intervention of pure oxygen was given in the solution to examine the oxygenation changes in the phantom, and 3 mL of human blood was added twice to show the changes in [Hb(total)]. The error calculation has shown that even a small variation (0.01 cm(-1) mM(-1)) in extinction coefficients can produce appreciable relative errors in quantification of Delta[HbO(2)], Delta[Hb] and Delta[Hb(total)]. We have also observed that the error of Delta[Hb(total)] is not always larger than those of Delta[HbO(2)] and Delta[Hb]. This study concludes that we need to be aware of any variation in haemoglobin extinction coefficients, which could result from changes in temperature, and to utilize corresponding animal's haemoglobin extinction coefficients for the animal experiments, in order to obtain more accurate values of Delta[HbO(2)], Delta[Hb] and Delta[Hb(total)] from in vivo tissue measurements.
Motion artifact reduction in photoplethysmography, and therefore by implication in pulse oximetry, is achieved with a novel nonlinear methodology. The physical origins of the photoplethysmographic signals are explored in relation to a nonlinear measure of the observed intensity fluctuations. It is demonstrated that the nonlinearity renormalizes the received pulsations with optical information in a manner that aids physical interpretation. A heuristic physical model for the motion artifact is introduced and experimentally justified, with an inversion for artifact reduction being simplified by the nonlinearity. A practical implementation technique is discussed with emphasis placed on the resultant rescaling of the static and the dynamic portions of the signals. It is noted that this implementation also has the desirable effect of reducing any residual ambient artifact. The scope and power of this methodology is investigated with the presentation of results from a practical electronic system.
The extreme conditions of combat and multi-casualty rescue often make field triage difficult and put the medic or first responder at risk. In an effort to improve Reid triage, we have developed an automated remote triage system called ARTEMIS for use in the battlefield or disaster zone. This preliminary research seeks to empirically demonstrate that the Nonin forehead reflectance pulse oximeter is a viable sensor for measuring essential physiological parameters used in automated field triage systems such as ARTEMIS.
OBJECTIVE
To examine if initial transcutaneous oxygen saturation (SaO2) in the Emergency Department (ED) is a useful predictor of prolonged frequent bronchodilator therapy (FBT) in children with acute asthma.
METHODS
Prospective cohort study of 273 children 1–17 years requiring systemic corticosteroids. Patients were categorized as needing FBT for >4 hours (n=166) versus less (n=107), and >12 hours (n=79) versus less (n=194). Multiple logistic regression determined the association between various levels of SaO2 and these outcomes.
RESULTS
Baseline SaO2 remains a significant independent predictor of FBT for >4 hours (OR=0.81) and >12 hours (OR=0.84). 91% of patients with SaO2 of 90–91% had FBT >4 hours, and 80% of patients with SaO2 <90% had FBT >12 hours. Children with SaO2 <92% are 14.7 and 12.0 times more likely to require FBT for >4 hours and >12 hours, respectively, than those with SaO2 of 98–100%. The interval likelihood ratios (LR) for FBT >4 hours were 12.3 for SaO2 <90%, 6.5 for 90–91%, but only 1.8 for 92–93%. The LR for FBT >12 hours decreased from 9.8 for SaO2 <90% to 3.5 for SaO2 of 90–91%.
CONCLUSIONS
SaO2 is a useful predictor of FBT>4 hours if it is <92%, and >12 hours if it is <90%.
We present HemaApp, a smartphone application that noninvasively monitors blood hemoglobin concentration using the smartphone's camera and various lighting sources. Hemoglobin measurement is a standard clinical tool commonly used for screening anemia and assessing a patient's response to iron supplement treatments. Given a light source shining through a patient's finger, we perform a chromatic analysis, analyzing the color of their blood to estimate hemoglobin level. We evaluate HemaApp on 31 patients ranging from 6 -- 77 years of age, yielding a 0.82 rank order correlation with the gold standard blood test. In screening for anemia, HemaApp achieve a sensitivity and precision of 85.7% and 76.5%. Both the regression and classification performance compares favorably with our control, an FDA-approved noninvasive hemoglobin measurement device. We also evaluate and discuss the effect of using different kinds of lighting sources.
Background:
Universal access to pulse oximetry worldwide is often limited by cost and has substantial public health consequences. Low-cost pulse oximeters have become increasingly available with limited regulatory agency oversight. The accuracy of these devices often has not been validated, raising questions about performance.
Methods:
The accuracy of 6 low-cost finger pulse oximeters during stable arterial oxygen saturations (SaO2) between 70% and 100% was evaluated in 22 healthy subjects. Oximeters tested were the Contec CMS50DL, Beijing Choice C20, Beijing Choice MD300C23, Starhealth SH-A3, Jumper FPD-500A, and Atlantean SB100 II. Inspired oxygen, nitrogen, and carbon dioxide partial pressures were monitored and adjusted via a partial rebreathing circuit to achieve 10 to 12 stable target SaO2 plateaus between 70% and 100% and PaCO2 values of 35 to 45 mm Hg. Comparisons of pulse oximeter readings (SpO2) with arterial SaO2 (by Radiometer ABL90 and OSM3) were used to calculate bias (SpO2 - SaO2) mean, precision (SD of the bias), and root mean square error (ARMS).
Results:
Pulse oximeter readings corresponding to 536 blood samples were analyzed. Four of the 6 oximeters tested showed large errors (up to -6.30% mean bias, precision 4.30%, 7.53 ARMS) in estimating saturation when SaO2 was reduced <80%, and half of the oximeters demonstrated large errors when estimating saturations between 80% and 90%. Two of the pulse oximeters tested (Contec CMS50DL and Beijing Choice C20) demonstrated ARMS of <3% at SaO2 between 70% and 100%, thereby meeting International Organization for Standardization (ISO) criteria for accuracy.
Conclusions:
Many low-cost pulse oximeters sold to consumers demonstrate highly inaccurate readings. Unexpectedly, the accuracy of some low-cost pulse oximeters tested here performed similarly to more expensive, ISO-cleared units when measuring hypoxia in healthy subjects. None of those tested here met World Federation of Societies of Anaesthesiologists standards, and the ideal testing conditions do not necessarily translate these findings to the clinical setting. Nonetheless, further development of accurate, low-cost oximeters for use in clinical practice is feasible and, if pursued, could improve access to safe care, especially in low-income countries.
This study investigates the usability of the smartphone camera for the evaluation of arterial blood oxygenation (SpO2%). The advantage of this solution derives from the pervasiveness of the smartphone that makes available the evaluation of the SpO2% everywhere. Differently from the pulse oximeter, which uses well-defined wavelength light, the smartphone uses Light Emitting Diodes as a light source to evaluate the SpO2%. The change of the light intensity in the Red and Green colour channels in the video frames of the patient fingertip are properly processed. Two PPG signals are obtained at the wavelengths 600nm and 940nm, respectively. These two PPGs are used to evaluate the SpO2% without calibration coefficients and independently of the smartphone hardware and skin characteristics. Experimental tests are performed to compare the proposed procedure with respect to a commercial pulse oximeter and gas chromatograph. The experimental tests assess the effectiveness of the proposal.
Low level laser radiation therapy is effective in a number of clinical situations (e,g. pain relief, wound healing, sports medicine), but the photobiological basis of this therapy is not well-understood, Since both visible and infrared radiations have been shown to be beneficial in such therapies, and since these two radiations differ dramatically in their photochemical and photophysical properties, how can they produce similar results clinically? I propose a modification of the model of Karu1 to explain this. In her model. visible light produces photochemical changes in photoreceptors in the mitochondria, which alter metabolism, which leads to signal transduction to other parts of the cell (including membranes), which finally leads to the photoresponse (i.e, biostimulation). While visible light probably starts the cascade of metabolic events at the level of the respiratory chain of the mitochondria through photochemical events (probably the photoactivation of enzymes), I propose that because of the photochemical and photophysical properties of infrared radiation, infrared radiation starts the cascade of metabolic events by photophysical effects on the membranes (probably the Ca++ channels), Action spectra are needed to quantitate the relative effectiveness of the different wavelengths of radiation, since this can help to identify the photoreceptors for the photobiological response, and to establish the optimum conditions (i,e. wavelength, dose, and treatment schedule) for a particular therapy
Purpose:
Diabetic retinopathy is characterized by retinal vascular impairment resulting in retinal hypoxia. The disease can be treated by retinal photocoagulation, but the mechanism of action of this treatment is unknown. Therefore, it is of interest to investigate whether the effects of retinal photocoagulation are related to changes in oxygen saturation.
Methods:
Retinal oximetry and diameter measurements were performed on larger retinal arterioles and venules in 220 eyes from 149 patients with diabetic maculopathy (DM) and proliferative diabetic retinopathy (PDR) before, immediately after, and 3 months after photocoagulation treatment.
Results:
Before treatment oxygen saturation was increased in retinal venules in DM patients to result in reduced arteriovenous (AV) saturation difference, and was increased in arterioles and venules in PDR patients to result in a normal AV saturation difference. Immediately after treatment the oxygen saturation in both groups was unchanged in retinal arterioles and increased in retinal venules resulting in a reduced AV saturation difference. Three months after treatment arterial and venous saturations were increased, but the AV saturation difference was not different from the pretreatment level. In both patient groups vascular diameters had decreased 3 months after treatment, which was significant for venules in the PDR group.
Conclusions:
The effects of retinal photocoagulation on diabetic retinopathy are not correlated with changes in oxygen saturation in larger retinal vessels.
This paper proposes a method to detect pressure asserted on a mobile phone by utilizing the back camera and flash on the phone. There is a gap between the palm and camera when the phone is placed on the palm. This allows the light from the flashlight to be reflected to the camera. However, when pressure is applied on the phone, the gap will reduce, reducing the brightness captured by the camera. This phenomenon is applied to detect two gestures: pressure applied on the screen and pressure applied when user squeezes the phone. We also conducted an experiment to detect the change in brightness level depending on the amount of force asserted on the phone when it is placed in two positions: parallel to the palm and perpendicular to the palm. The results show that when the force increases, the brightness level decreases. Using the phones ability to detect fluctuations in brightness, various pressure interaction applications such as for gaming purposes may be developed.
In case of a healthy subject the normal SpO2 value is 97 2% on see level. Modern, finger probe based pulse oximeters are measuring the SpO2 level with 1-2% error. The dispersion be- tween subjects can reach 4%, thus such accuracy is not really demanded by the majority of clinicians. Moreover, in case of fetal pulse oximetry 5% measuring error is accepted. Consider- ing these factors we investigated the feasibility of a non-invasive calibration method with a self-developed pulse oximeter. This method is carried out without blood sampling. Pulse oximeters are measuring the R rate, which is proportional to the SpO2 value. Calibrating an oximeter means finding the function be- tween the R and SpO2. A calibrated pulse oximeter was used as reference. In the case of every subject 15 minutes long measure- ments were performed. The reference device and our oximeter were attached to the subject at the same time, while artificial air with 14% oxygen content was inhaled by the subject for ten minutes. The SpO2 was measured by the reference oximeter and the R rate by our oximeter. Based on 511 measured data pairs the relationship was determined between 86-100%. The rela- tionship was estimated by linear regression. Although the orig- inal relation is non-linear, linear estimation can be used in this small range of SpO2 with good accuracy. The average error of the calibrated device is 2.76%, which is appropriate in medi- cal practice. This method is easier and cheaper as the invasive calibration, but the calibrated device will have slightly bigger measuring error.
We consider the problem of finding a solution of a constrained (and not nec-essarily square) system of equations, i.e., we consider systems of nonlinear equations and want to find a solution that belongs to a certain feasible set. To this end, we present two Levenberg-Marquardt-type algorithms that differ in the way they compute their search di-rections. The first method solves a strictly convex minimization problem at each iteration, whereas the second one solves only one system of linear equations in each step. Both meth-ods are shown to converge locally quadratically under an error bound assumption that is much weaker than the standard nonsingularity condition. Both methods can be globalized in an easy way. Some numerical results for the second method indicate that the algorithm works quite well in practice.
We consider the problem of finding a solution of a constrained (and not necessarily square) system of equations, i.e., we consider systems of nonlinear equations and want to find a solution that belongs to a certain feasible set. To this end, we present two Levenberg–Marquardt-type algorithms that differ in the way they compute their search directions. The first method solves a strictly convex minimization problem at each iteration, whereas the second one solves only one system of linear equations in each step. Both methods are shown to converge locally quadratically under an error bound assumption that is much weaker than the standard nonsingularity condition. Both methods can be globalized in an easy way. Some numerical results for the second method indicate that the algorithm works quite well in practice.
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. The authors describe the application of RANSAC to the Location Determination Problem (LDP): given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing and analysis conditions. Implementation details and computational examples are also presented
There is uncertainty about the best method of withdrawing supplemental oxygen in babies with chronic neonatal lung disease (CNLD). Some authors advocate withdrawal of oxygen in the day, but continuing supplementation during sleep, based on early work suggesting that oxygen saturations are lower during sleep, which did not accord with our clinical impression. We re-examined the hypothesis that babies have lower saturations while asleep.
We studied infants with CNLD during the day, while awake and asleep. We recorded video with simultaneous real-time capture of oxygen saturation (SpO2), heart rate and plethysmographic waveform from pulse oximetry. Behavioural state was scored using observation and video and classified as awake (feeding, active or quiet) or sleep.
Thirteen infants had analysable data, although one had strikingly lower SpO2 values while awake and was excluded from analysis. The infants had a median gestation of 26 weeks and were studied at a median (range) postmenstrual age of 66 (37-130) weeks, for 229 (89-330) min. Mean SpO2 was 97.6% during sleep and 97.0% awake (p=0.011).
Babies with CNLD have lower oxygen saturation while awake. There is no physiological justification for increasing oxygen during sleep, or withdrawing selectively during the daytime, although larger studies are needed to confirm this finding.
Oxygen is critical for multicellular existence. Its reduction to water by the mitochondrial electron transport chain helps supply the metabolic demands of human life. The incompletely reduced, reactive oxygen byproducts of this reaction, however, can be quite toxic. In this review, we explore the mechanisms responsible for maintaining oxygen homeostasis and the consequences of their dysfunction. With an eye toward defining clinical care guidelines for the management of critically ill neonates, we present evidence describing the role of physiologic hypoxia during development and the adverse consequences of hyperoxia in-term as well as preterm infants.
Reduction of motion artifact and power saving are crucial in designing a wearable pulse oximeter for long-term telemedicine application. In this paper, a novel algorithm, minimum correlation discrete saturation transform (MCDST) has been developed for the estimation of arterial oxygen saturation (SaO2), based on an optical model derived from photon diffusion analysis. The simulation shows that the new algorithm MCDST is more robust under low SNRs than the clinically verified motion-resistant algorithm discrete saturation transform (DST). Further, the experiment with different severity of motions demonstrates that MCDST has a slightly better performance than DST algorithm. Moreover, MCDST is more computationally efficient than DST because the former uses linear algebra instead of the time-consuming adaptive filter used by latter, which indicates that MCDST can reduce the required power consumption and circuit complexity of the implementation. This is vital for wearable devices, where the physical size and long battery life are crucial.
We determined the millimolar absorptivities of the four clinically relevant derivatives of fetal and adult human hemoglobin in the visible and near-infrared spectral range (450-1000 nm). As expected, spectral absorption curves of similar shape were found, but the small differences between fetal and adult hemoglobin absorptivity were important enough that they should be taken into account in multicomponent analysis of hemoglobin derivatives. Common pulse oximeters, however, involving light of 660 and 940 nm, are so insensitive to the presence of fetal hemoglobin that they can be used safely in neonates. The error in pulse oximetry caused by the presence of carboxyhemoglobin is insubstantial, but methemoglobin gives either an understimation or an overestimation at high or low oxygen saturation, respectively, the turning point being near 70% saturation.
The published studies of pulse oximeter performance under conditions of normal, high and low saturation, exercise, poor signal quality and cardiac arrhythmia are reviewed. Most pulse oximeters have an absolute mean error of less than 2% at normal saturation and perfusion; two-thirds have a standard deviation (SD) of less than 2%, and the remainder an SD of less than 3%. Some pulse oximeters tend to read 100% with fractional saturations of 97-98%. Pulse oximeters may be suitable hyperoxic alarms for neonates if the alarm limit chosen is directly validated for each device. Pulse oximeters are poorly calibrated at low saturations and are generally less accurate and less precise than at normal saturations; nearly 30% of 244 values reviewed were in error by more than 5% at saturations of less than 80%. Ear, nose and forehead probes respond more rapidly to rapid desaturation than finger probes, but are generally less accurate and less precise. Ear oximetry may be inaccurate during exercise. Low signal quality can result in failure to present a saturation reading, but data given with low signal quality warning messages are generally no less accurate than those without. Cardiac arrhythmias do not decrease accuracy of pulse oximeters so long as saturation readings are steady.
There is no absolute reference for oxygen saturation, although multiwavelength in vitro oximeters are accepted as the 'gold standard'. Regardless of whether fractional or functional saturation is used by manufacturers to calibrate their oximeters, evaluation against fractional saturation is recommended since this is the clinically relevant variable. The use of standard notation and comparisons based on bias and precision is recommended. The accuracy of pulse oximetry is intrinsically limited by the use of only two wavelengths, and is dependent on the initial calibration population. The empirical algorithms used to convert the signal to its 'readout value' and the quality control of hardware may both be important sources of variability between oximeters. Change in blood temperature may introduce errors in pulse oximeter and in vitro oximeter saturation readings, but these will be clinically insignificant. Changes in blood pH should not decrease pulse oximetry accuracy.
We tested the hypothesis that left tracheal pulse oximetry (SpO(2)) is more accurate than finger SpO(2) when compared with oxygen saturation from arterial blood samples (SaO(2)) in anesthetized patients with normal thoracic anatomy. We also tested the hypothesis that tracheal oximetry readings are primarily derived from the tracheal mucosa. We studied 20 hemodynamically stable, well oxygenated, anesthetized patients with normal anatomy (ASA physical status I-III, 18-80 yr old). A single-use pediatric pulse oximeter was attached to the left lateral surface of a tracheal tube cuff. Tracheal and finger SpO(2) (dominant index finger), and SaO(2) (nondominant radial artery) were taken with the intracuff pressure at 0-60 cm H(2)O. Tracheal SpO(2) was the same as SaO(2) at an intracuff pressure of 10-60 cm H(2)O, but was less when the intracuff pressure was zero (P<0.0001). Tracheal SpO(2) was higher than finger SpO(2) at an intracuff pressure of 10-60 cm H(2)O (all: P <0.001), but was lower when the intracuff pressure was zero (P< 0.0001). SaO(2) was always higher than finger SaO(2) (P<0.0001). Tracheal SpO(2) was lower at an intracuff pressure of zero (P< 0.0001), but was otherwise similar over the range of intracuff pressures. SaO(2) and finger SpO(2) did not vary with intracuff pressure. Tracheal SpO(2) agrees more closely with SaO(2) than finger SpO(2) at an intracuff pressure of 10-60 cm H(2)O (mean difference < 0.2%). We conclude that left tracheal SpO(2) is feasible and provides similar readings to arterial blood samples and more accurate readings than finger oximetry in hemodynamically stable, well oxygenated, anesthetized patients with normal thoracic anatomy. Tracheal oximetry readings are not primarily derived from the tracheal mucosa. The technique merits further evaluation.
To examine if the initial oxygen saturation (SaO2) in the Emergency Department is a useful predictor of prolonged frequent bronchodilator therapy (FBT) in children with acute asthma.
Prospective cohort study of 273 children, 1 to 17 years of age, requiring systemic corticosteroids. Patients were categorized as needing FBT for >4 hours (n=166) versus >4 hours (n=107) and >12 hours (n=79) versus >12 hours (n=194). Multiple logistic regression determined the association between SaO2 and these outcomes.
Baseline SaO2 remains a significant independent predictor of FBT for >4 hours (OR=0.81) and >12 hours (OR=0.84); 91% of patients with SaO2 of 90% to 91% had FBT >4 hours and 80% of patients with SaO2 of < or =89% had FBT >12 hours. Children with SaO2 of < or =91% are 14.7 and 12.0 times more likely to require FBT for >4 hours and >12 hours, respectively, than those with SaO2 of 98% to 100%. The interval likelihood ratios for FBT >4 hours were 12.3 for SaO2 of < or =89%, 6.5 for 90% to 91%, but only 1.8 for 92% to 93%. The likelihood ratios for FBT >12 hours decreased from 9.8 for SaO2 of < or =89% to 3.5 for SaO2 of 90% to 91%.
SaO2 is a useful predictor of FBT >4 hours if it is < or =91% and of FBT >12 hours if it is < or =89%.
The BTS/SIGN guideline recommends oxygen saturation (SaO2) monitoring as an objective measure of acute asthma severity, particularly in children, in both primary and secondary care. We assessed the availability and use of SaO2 monitoring for acute asthma assessment in primary care.
Fax and telephone questionnaire of Primary Care services in the Edinburgh region to assess use of SaO2 monitoring in the past 24 months, in association with a 24-month retrospective assessment of A&E attendances with acute wheeze. Children over 12 months of age registered with eligible general practices attending A&E with wheeze and/or asthma were included.
There were replies from 103 general practices (100%) and eight Out-of-hours cooperatives (100%). Oxygen saturation monitoring was available in four general practices (3.9%) and three Out-of-hours cooperatives (37.5%). 1408 children attended A&E with wheeze/asthma, 721 referred by primary care. Oxygen saturation monitoring was available to 7.9% of A&E attendees from primary care, but documented in only 1.8% of primary care referrals.
SaO2 monitoring is not widely available in primary care and is infrequently used for the assessment of acute asthma. SaO2 measurement as an adjunct to clinical assessment of asthma in primary care needs encouragement.
This study investigated whether a tensioning headband that applies up to 20 mmHg pressure over a forehead pulse oximetry sensor could improve arterial hemoglobin oxygen saturation reading accuracy in presence of venous pooling and pulsations at the forehead site.
Healthy volunteers were studied breathing room air in supine and various levels of negative incline (Trendelenburg position) using the forehead sensor with the headband adjusted to its maximum and minimum recommended pressure limits. Saturation readings obtained from the forehead sensor with the subjects supine and the headband in place were used as a baseline to compare the effects of negative incline on reading accuracy when using and not using the headband. Occurrences of false low-saturation readings detected by forehead sensors were compared with those from digit sensors.
No difference was observed between saturation readings obtained from the forehead sensor in supine and negative incline positions when the headband was applied. Forehead sensor readings obtained while subjects were inclined and the headband was not used were significantly lower (P < 0.05) than the supine readings. There was no statistically significant difference between the digit and forehead sensor in reporting false low-saturation readings when the headband was applied, regardless of body incline.
Application of up to 20 mmHg pressure on the forehead pulse oximetry sensor using an elastic tensioning headband significantly reduced reading errors and provided consistent performance when subjects were placed between supine and up to 15 degrees head-down incline (Trendelenburg position).
Pulse oximeters are mainstays for acquiring blood oxygen saturation in static environments such as hospital rooms. However, motion artifacts prevent their broad in wearable, ambulatory environments. To this end, we present a novel algorithm to separate the motion artifacts from plethysmographic data gathered by pulse oximeters. This algorithm, based on the Beer-Lambert law, requires photoplethysmographic data acquired at three excitation wavelengths. The algorithm can calculate venous blood oxygen saturation (SvO2) as well as arterial blood oxygen saturation (SaO2). Preliminary results indicate that the extraction of the venous signal, which is assumed to be most affected by motions, is successful with data acquired from a reflectance-mode sensor.
A new forehead noninvasive oxygen saturation sensor may improve signal quality in patients with low cardiac index.
To examine agreement between oxygen saturation values obtained by using digit-based and forehead pulse oximeters with arterial oxygen saturation in patients with low cardiac index.
A method-comparison study was used to examine the agreement between 2 different pulse oximeters and arterial oxygen saturation in patients with low cardiac index. Readings were obtained from a finger and a forehead sensor and by analysis of a blood sample. Bias, precision, and root mean square differences were calculated for the digit and forehead sensors. Differences in bias and precision between the 2 noninvasive devices were evaluated with a t test (level of significance P<.05).
Nineteen patients with low cardiac index (calculated as cardiac output in liters per minute divided by body surface area in square meters; mean 1.98, SD 0.34) were studied for a total of 54 sampling periods. Mean (SD) oxygen saturations were 97% (2.4) for blood samples, 96% (3.2) for the finger sensor, and 97% (2.8) for the forehead sensor. By Bland Altman analysis, bias +/- precision was -1.16 +/- 1.62% for the digit sensor and -0.36 +/- 1.74% for the forehead sensor; root mean square differences were 1.93% and 1.70%, respectively. Bias and precision differed significantly between the 2 devices; the forehead sensor differed less from the blood sample.
In patients with low cardiac index, the forehead sensor was better than the digit sensor for pulse oximetry.
Measurement of pulse oximetry (Spo(2)) is often impaired in critically ill patients. Forehead reflectance oximetry, the Max-Fast (Nellcor, Pleasanton, CA), may be less susceptible to poor tissue perfusion and could improve accuracy of oxygen saturation measurement. The objective of this study was to evaluate the use of forehead oximetry measures in critically ill surgical/trauma patients.
A prospective interventional study of 30 critically ill surgical/trauma patients at risk for decreased peripheral perfusion, as evidenced by need for vasopressor support (24 patients), transfusion of more than 6 unit packed cells in 24 hours (two patients), or an inability to obtain consistent saturation from a digit sensor (four patients), compared forehead and digit-based oximeter Spo(2) readings with co-oximetry (Sao(2)) measurements from arterial blood samples. Sao(2) values were converted to functional oxygen saturation (SO(2)) measurements for the final comparison. Patients were fitted with forehead (Nellcor Max-Fast) and digit (Nellcor Max A; digit 1) sensors connected to Nellcor OxiMax N-595 oximeters and a digit sensor (Nellcor Max A; digit 2) connected to a multiparameter monitor (Philips CMS [Andover, MA]). Three measurements of Sao(2) were obtained from each subject over a 24-hour time period, and simultaneous measurements of Spo(2) were recorded from the three monitors.
The three Spo(2) measurements (forehead, digit 1, and digit 2) were compared with SO(2) values using the Bland-Altman method to assess agreement. Forehead measurements demonstrated a mean bias of -1.39, whereas digit 1 was -2.61 and digit 2 was -3.84. Pearson correlations (r) for forehead, digit 1, and digit 2 with SO(2) were .834, .433, and .254, respectively. There were fewer unsuccessful measurements with the forehead oximetry technique.
Forehead sensors improve measurement of oxygen saturation in critically ill surgical/trauma patients at risk for decreased peripheral perfusion.
Finger clip pulse oximetry sensors are commonly used to obtain functional oxygen saturation readings (S(pO2)), but these sensors may perform poorly if the digit is poorly perfused or there is excessive hand movement. I have increasingly witnessed clinicians obtaining S(pO2) readings by placing the finger clip sensor on the patient's ear when an S(pO2) reading cannot be obtained from a finger.
Determine if reliable S(pO2) readings can be obtained from a finger clip sensor placed on the ear.
This was a prospective study with patients undergoing pulmonary function testing. The calculated functional oxygen saturation values from arterial blood gas analysis (S(aO2)) were compared with S(pO2) readings from a finger clip sensor placed on a finger (finger S(pO2)) and on the upper portion of an ear (ear S(pO2)). S(pO2) data were included in the study only if (1) the pulse rate from finger S(pO2) and ear S(pO2) differed by < or = 5 beats/min and (2) the photoplethysmographic waveform was stable and acceptable.
Data were obtained from 30 adult white patients. The number of S(pO2) readings that differed from the S(aO2) values by > or = 3% were 1 (3.3%) finger S(pO2) reading and 24 (80%, 95% CI 61%-92%) ear S(pO2) readings (p < or = 0.001). Bland-Altman analysis showed better agreement between S(aO2) and finger S(pO2) (mean +/- 2 SD limits of agreement -2.35 to 2.35) than between S(aO2) and ear S(pO2) (limits of agreement -7.24 to -0.08) or finger S(pO2) and ear S(pO2) (limits of agreement -7.56 to -0.23).
A pulse oximeter finger clip sensor placed on the ear does not provide clinically reliable S(pO2) readings.
Low levels of oxygen (O2) occur naturally in developing embryos. Cells respond to their hypoxic microenvironment by stimulating several hypoxia-inducible factors (and other molecules that mediate O2 homeostasis), which then coordinate the development of the blood, vasculature, placenta, nervous system and other organs. Furthermore, embryonic stem and progenitor cells frequently occupy hypoxic 'niches' and low O2 regulates their differentiation. Recent work has revealed an important link between factors that are involved in regulating stem and progenitor cell behaviour and hypoxia-inducible factors, which provides a molecular framework for the hypoxic control of differentiation and cell fate. These findings have important implications for the development of therapies for tissue regeneration and disease.
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