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Oximeters have significantly evolved since their invention and are essential for monitoring chronic diseases in home care. However, commercial models can present an economic barrier. Therefore, we conducted a review of the use of low-cost pulse oximeters in the home care of patients with respiratory diseases. Our review included studies addressing oxygen saturation and heart rate monitoring in adults, focusing on the use of portable devices. Our search identified advances in vital signs monitoring that could provide accessible solutions for non-clinical settings. Although there are challenges related to clinical validation and accuracy, these oximeters may improve medical care, particularly in resource-limited areas. As a result, the accessibility of these devices opens up new possibilities for patients with chronic respiratory diseases in home care, enabling regular self-monitoring and increasing control over their health. [ABSTRACT FROM AUTHOR]

Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse oximeters can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative noninvasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate the prominent ECG features based on pH estimation that could potentially be used to explore the noninvasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset used contained 274 segments of ECG and pH values recorded simultaneously. After preprocessing the data, principal component analysis and the Pan–Tompkins algorithm were used for each segment to determine the most significant ECG cycle and to compute the ECG features. Descriptive statistics were performed to describe the main properties of the processed dataset. A Kruskal–Wallis nonparametric test was then used to analyze differences between the asphyxiated and non-asphyxiated groups. Finally, a Dunn–Šidák post hoc test was used for individual comparison among the mean ranks of all groups. The findings of this study showed that ECG features (T/QRS, T Amplitude, Tslope, Tslope/T, Tslope/|T|, HR, QT, and QTc) based on pH estimation differed significantly (p < 0.05) in asphyxiated neonates. All these key ECG features were also found to be significantly different between the two groups. [ABSTRACT FROM AUTHOR]

Pulse oximeters are widely used in hospitals and homes for measurement of blood oxygen saturation level (SpO2) and heart rate (HR). Concern has been raised regarding a possible bias in obtaining pulse oximeter measurements from different fingertips and the potential effect of skin pigmentation (white, brown, and dark). In this study, we obtained 600 SpO2 measurements from 20 volunteers using three UK NHS-approved commercial pulse oximeters alongside our custom-developed sensor, and used the Munsell colour system (5YR and 7.5YR cards) to classify the participants' skin pigmentation into three distinct categories (white, brown, and dark). The statistical analysis using ANOVA post hoc tests (Bonferroni correction), a Bland–Altman plot, and a correlation test were then carried out to determine if there was clinical significance in measuring the SpO2 from different fingertips and to highlight if skin pigmentation affects the accuracy of SpO2 measurement. The results indicate that although the three commercial pulse oximeters had different means and standard deviations, these differences had no clinical significance. [ABSTRACT FROM AUTHOR]

Dissolved Oxygen (DO) in water enables marine life. Measuring the prevalence of DO in a body of water is an important part of sustainability efforts because low oxygen levels are a primary indicator of contamination and distress in bodies of water. Therefore, aquariums and aquaculture of all types are in need of near real-time dissolved oxygen monitoring and spend a lot of money on purchasing and maintaining DO meters that are either expensive, inefficient, or manually operated—in which case they also need to ensure that manual readings are taken frequently which is time consuming. Hence a cost-effective and sustainable automated Internet of Things (IoT) system for this task is necessary and long overdue. DOxy, is such an IoT system under research and development at Santa Clara University's Ethical, Pragmatic, and Intelligent Computing (EPIC) Laboratory which utilizes cost-effective, accessible, and sustainable Sensing Units (SUs) for measuring the dissolved oxygen levels present in bodies of water which send their readings to a web based cloud infrastructure for storage, analysis, and visualization. DOxy's SUs are equipped with a High-sensitivity Pulse Oximeter meant for measuring dissolved oxygen levels in human blood, not water. Hence a number of parallel readings of water samples were gathered by both the High-sensitivity Pulse Oximeter and a standard dissolved oxygen meter. Then, two approaches for relating the readings were investigated. In the first, various machine learning models were trained and tested to produce a dynamic mapping of sensor readings to actual DO values. In the second, curve-fitting models were used to produce a successful conversion formula usable in the DOxy SUs offline. Both proved successful in producing accurate results. [ABSTRACT FROM AUTHOR]

The monitoring of oxygen therapy when patients are admitted to medical and surgical wards could be important because exposure to excessive oxygen administration (EOA) may have fatal consequences. We aimed to investigate the association between EOA, monitored by wireless pulse oximeter, and nonfatal serious adverse events (SAEs) and mortality within 30 days. We included patients in the Capital Region of Copenhagen between 2017 and 2018. Patients were hospitalized due to acute exacerbation of chronic obstructive pulmonary disease (AECOPD) or after major elective abdominal cancer surgery, and all were treated with oxygen supply. Patients were divided into groups by their exposure to EOA: no exposure, exposure for 1–59 min or exposure over 60 min. The primary outcome was SAEs or mortality within 30 days. We retrieved data from 567 patients for a total of 43,833 h, of whom, 63% were not exposed to EOA, 26% had EOA for 1–59 min and 11% had EOA for ≥60 min. Nonfatal SAEs or mortality within 30 days developed in 24%, 12% and 22%, respectively, and the adjusted odds ratio for this was 0.98 (95% CI, 0.96–1.01) for every 10 min. increase in EOA, without any subgroup effects. In conclusion, we did not observe higher frequencies of nonfatal SAEs or mortality within 30 days in patients exposed to excessive oxygen administration. [ABSTRACT FROM AUTHOR]

Although smartwatches are not considered medical devices, experimental validation of their accuracy in detecting hypoxemia is necessary due to their potential use in monitoring conditions manifested by a prolonged decrease in peripheral blood oxygen saturation (SpO2), such as chronic obstructive pulmonary disease, sleep apnea syndrome, and COVID-19, or at high altitudes, e.g., during sport climbing, where the use of finger-sensor-based pulse oximeters may be limited. The aim of this study was to experimentally compare the accuracy of SpO2 measurement of popular smartwatches with a clinically used pulse oximeter according to the requirements of ISO 80601-2-61. Each of the 18 young and healthy participants underwent the experimental assessment three times in randomized order—wearing Apple Watch 8, Samsung Galaxy Watch 5, or Withings ScanWatch—resulting in 54 individual experimental assessments and complete datasets. The accuracy of the SpO2 measurements was compared to that of the Radical-7 (Masimo Corporation, Irvine, CA, USA) during short-term hypoxemia induced by consecutive inhalation of three prepared gas mixtures with reduced oxygen concentrations (14%, 12%, and 10%). All three smartwatch models met the maximum acceptable root-mean-square deviation (≤4%) from the reference measurement at both normal oxygen levels and induced desaturation with SpO2 less than 90%. Apple Watch 8 reached the highest reliability due to its lowest mean bias and root-mean-square deviation, highest Pearson correlation coefficient, and accuracy in detecting hypoxemia. Our findings support the use of smartwatches to reliably detect hypoxemia in situations where the use of standard finger pulse oximeters may be limited. [ABSTRACT FROM AUTHOR]

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network. [ABSTRACT FROM AUTHOR]

Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents a novel method for diagnosing obstructive sleep apnea using a transformer neural network with learnable positional encoding, which outperforms existing state-of-the-art solutions. This approach has the potential to improve the diagnostic performance of oximetry for obstructive sleep apnea and reduce the time and costs associated with traditional polysomnography. Contrary to existing approaches, our approach performs annotations at one-second granularity. Allowing physicians to interpret the model's outcome. In addition, we tested different positional encoding designs as the first layer of the model, and the best results were achieved using a learnable positional encoding based on an autoencoder with structural novelty. In addition, we tried different temporal resolutions with various granularity levels from 1 to 360 s. All experiments were carried out on an independent test set from the public OSASUD dataset and showed that our approach outperforms current state-of-the-art solutions with a satisfactory AUC of 0.89, accuracy of 0.80, and F1-score of 0.79. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the signal. This transition presents several challenges related to complexity, accuracy, and reliability of algorithms. To address these challenges, anomaly detection stages can be employed to increase the accuracy and reliability of estimated parameters. Powerful algorithms, such as lightweight machine learning (ML) algorithms, can be used for anomaly detection in multi-wavelength PPG (MW-PPG). The main contributions of this paper are (a) proposing a set of features with high information gain for anomaly detection in MW-PPG signals in the classification context, (b) assessing the impact of window size and evaluating various lightweight ML models to achieve highly accurate anomaly detection, and (c) examining the effectiveness of MW-PPG signals in detecting artifacts. [ABSTRACT FROM AUTHOR]

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study. [ABSTRACT FROM AUTHOR]

Pulse oximetry is a non-invasive method for measuring blood oxygen saturation. However, its detection scheme heavily relies on single-point measurements. If the oxygen saturation is measured at a single location, the measurements are influenced by the profile of illumination, spatial variations in blood flow, and skin pigment. To overcome these issues, imaging systems that measure the distribution of oxygen saturation have been demonstrated. However, previous imaging systems have relied on red and near-infrared illuminations with different profiles, resulting in inconsistent ratios between transmitted red and near-infrared light over space. Such inconsistent ratios can introduce fundamental errors when calculating the spatial distribution of oxygen saturation. In this study, we developed a novel illumination system specifically designed for a pulse oximetry imaging system. For the illumination system, we customized the integrating sphere by coating a mixture of barium sulfate and white paint inside it and by coupling eight red and eight near-infrared LEDs. The illumination system created identical patterns of red and near-infrared illuminations that were spatially uniform. This allowed the ratio between transmitted red and near-infrared light to be consistent over space, enabling the calculation of the spatial distribution of oxygen saturation. We believe our developed pulse oximetry imaging system can be used to obtain spatial information on blood oxygen saturation that provides insight into the oxygenation of the blood contained within the peripheral region of the tissue. [ABSTRACT FROM AUTHOR]

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers' physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%. [ABSTRACT FROM AUTHOR]

The recognition of hypoxia symptoms is a critical part of physiological training in military aviation. Acute exposure protocols have been designed in hypobaric chambers to train aircrews to recognize hypoxia and quickly take corrective actions. The goal of the acute hypoxia test is to know the time of useful consciousness and the minimal arterial oxygen saturation tolerated. Currently, there is no computer system specifically designed to analyze the physiological variables obtained during the test. This paper reports the development and analytical capabilities of a computational tool specially designed for these purposes. The procedure was designed using the Igor Pro 8.01 language, which processes oxygen saturation and heart rate signals. To accomplish this, three functional boards are displayed. The first allows the loading and processing of the data. The second generates graphs that allow for a rapid visual examination to determine the validity of individual records and calculate slopes on selected segments of the recorded signal. Finally, the third can apply filters to generate data groups for analysis. In addition, this tool makes it possible to propose new study variables that are derived from the raw signals and can be applied simultaneously to large data sets. The program can generate graphs accompanied by basic statistical parameters and heat maps that facilitate data visualization. Moreover, there is a possibility of adding other signals during the test, such as the oxygenation level in vital organs, electrocardiogram, or electroencephalogram, which illustrates the test's excellent potential for application in aerospace medicine and for helping us develop a better understanding of complex physiological phenomena. [ABSTRACT FROM AUTHOR]

On average, arterial oxygen saturation measured by pulse oximetry (SpO2) is higher in hypoxemia than the true oxygen saturation measured invasively (SaO2), thereby increasing the risk of occult hypoxemia. In the current article, measurements of SpO2 on 17 cyanotic newborns were performed by means of a Nellcor pulse oximeter (POx), based on light with two wavelengths in the red and infrared regions (660 and 900 nm), and by means of a novel POx, based on two wavelengths in the infrared region (761 and 820 nm). The SpO2 readings from the two POxs showed higher values than the invasive SaO2 readings, and the disparity increased with decreasing SaO2. SpO2 measured using the two infrared wavelengths showed better correlation with SaO2 than SpO2 measured using the red and infrared wavelengths. After appropriate calibration, the standard deviation of the individual SpO2−SaO2 differences for the two-infrared POx was smaller (3.6%) than that for the red and infrared POx (6.5%, p < 0.05). The overestimation of SpO2 readings in hypoxemia was explained by the increase in hypoxemia of the optical pathlengths-ratio between the two wavelengths. The two-infrared POx can reduce the overestimation of SpO2 measurement in hypoxemia and the consequent risk of occult hypoxemia, owing to its smaller increase in pathlengths-ratio in hypoxemia. [ABSTRACT FROM AUTHOR]

Filter face masks are Respiratory Protective Equipment designed to protect the wearer from various hazards, suit various health situations, and match the specific requirements of the wearer. Current traditional face masks have several limitations. In this paper, we present (ME) 2 , the Monitoring Equipment Mask Environment: an innovative reusable 3D-printed eco-sustainable mask with an interchangeable filter. (ME) 2 is equipped with multiple vital sensors on board, connected to a system-on-a-chip micro-controller with computational capabilities, Bluetooth communication, and a rechargeable battery that allows continuous monitoring of the wearer's vital signs. It monitors body temperature, heart rate, and oxygen saturation in a non-invasive, strategically positioned way. (ME) 2 is accompanied by a mobile application that provides users' health information. Furthermore, through Edge Computing Artificial Intelligence (Edge AI) modules, it is possible to detect an abnormal and early symptoms linked to possible pathologies, possibly linked to the respiratory or cardiovascular tract, and therefore perform predictive analysis, launch alerts, and recommendations. To validate the feasibility of embedded in-app Edge AI modules, we tested a machine learning model able to distinguish COVID-19 versus seasonal influenza using only vital signs. By generating new synthetic data, we confirm the highly reliable performances of such a model, with an accuracy of 94.80 % . [ABSTRACT FROM AUTHOR]

Early diagnosis and continuous monitoring of respiratory failure (RF) in the course of the most prevalent chronic cardio-vascular (CVD) and respiratory diseases (CRD) are a clinical, unresolved problem because wearable, non-invasive, and user-friendly medical devices, which could grant reliable measures of the oxygen saturation (SpO2) and heart rate (HR) in real-life during daily activities are still lacking. In this study, we investigated the agreement between a new medical wrist-worn device (BrOxy M) and a reference, medical pulseoximeter (Nellcor PM 1000N). Twelve healthy volunteers (aged 20–51 years, 84% males, 33% with black skin, obtaining, during the controlled hypoxia test, the simultaneous registration of 219 data pairs, homogeneously deployed in the levels of Sat.O2 97%, 92%, 87%, 82% [ISO 80601-2-61:2017 standard (paragraph EE.3)]) were included. The paired T test 0 and the Bland-Altman plot were performed to assess bias and accuracy. SpO2 and HR readings by the two devices resulted significantly correlated (r = 0.91 and 0.96, p < 0.001, respectively). Analyses excluded the presence of proportional bias. For SpO2, the mean bias was −0.18% and the accuracy (ARMS) was 2.7%. For HR the mean bias was 0.25 bpm and the ARMS3.7 bpm. The sensitivity to detect SpO2 ≤ 94% was 94.4%. The agreement between BrOxy M and the reference pulse oximeter was "substantial" (for SpO2 cut-off 94% and 90%, k = 0.79 and k = 0.80, respectively). We conclude that BrOxy M demonstrated accuracy, reliability and consistency in measuring SpO2 and HR, being fully comparable with a reference medical pulseoxymeter, with no adverse effects. As a wearable device, Broxy M can measure continually SpO2 and HR in everyday life, helping in detecting and following up CVD and CRD subjects. [ABSTRACT FROM AUTHOR]

The reliability of pulse oximetry is crucial, especially in cases of rapid changes in body oxygenation. In order to evaluate the performance of pulse oximeters during rapidly developing short periods of concurrent hypoxemia and hypercapnia, 13 healthy volunteers underwent 3 breathing phases during outdoor experiments (39 phases in total), monitored simultaneously by five different pulse oximeters. A significant incongruity in values displayed by the tested pulse oximeters was observed, even when the accuracy declared by the manufacturers were considered. In 28.2% of breathing phases, the five used devices did not show any congruent values. The longest uninterrupted congruent period formed 74.4% of total recorded time. Moreover, the congruent periods were rarely observed during the critical desaturation phase of the experiment. The time difference between the moments when the first and the last pulse oximeter showed the typical study endpoint values of SpO2 85% and 75% was 32.1 ± 23.6 s and 24.7 ± 19.3 s, respectively. These results suggest that SpO2 might not be a reliable parameter as a study endpoint, or more importantly as a safety limit in outdoor experiments. In the design of future studies, more parameters and continuous clinical assessment should be included. [ABSTRACT FROM AUTHOR]

Pulse oximetry is a noninvasive and continuous method for monitoring the blood oxygen saturation level. This paper presents the design and testing of a single-chip pulse oximeter fabricated in a 0.35 μm CMOS process. The chip includes photodiode, transimpedance amplifier, analogue band-pass filters, analogue-to-digital converters, digital signal processor and LED timing control. The experimentally measured AC and DC characteristics of individual circuits including the DC output voltage of the transimpedance amplifier, transimpedance gain of the transimpedance amplifier, and the central frequency and bandwidth of the analogue band-pass filters, show a good match (within 1%) with the circuit simulations. With modulated light source and integrated lock-in detection the sensor effectively suppresses the interference from ambient light and 1/f noise. In a breath hold and release experiment the single chip sensor demonstrates consistent and comparable performance to commercial pulse oximetry devices with a mean of 1.2% difference. The single-chip sensor enables a compact and robust design solution that offers a route towards wearable devices for health monitoring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The assessment of oxygen saturation in arterial blood by pulse oximetry (SpO2) is based on the different light absorption spectra for oxygenated and deoxygenated hemoglobin and the analysis of photoplethysmographic (PPG) signals acquired at two wavelengths. Commercial pulse oximeters use two wavelengths in the red and infrared regions which have different pathlengths and the relationship between the PPG-derived parameters and oxygen saturation in arterial blood is determined by means of an empirical calibration. This calibration results in an inherent error, and pulse oximetry thus has an error of about 4%, which is too high for some clinical problems. We present calibration-free pulse oximetry for measurement of SpO2, based on PPG pulses of two nearby wavelengths in the infrared. By neglecting the difference between the path-lengths of the two nearby wavelengths, SpO2 can be derived from the PPG parameters with no need for calibration. In the current study we used three laser diodes of wavelengths 780, 785 and 808 nm, with narrow spectral line-width. SaO2 was calculated by using each pair of PPG signals selected from the three wavelengths. In measurements on healthy subjects, SpO2 values, obtained by the 780-808 nm wavelength pair were found to be in the normal range. The measurement of SpO2 by two nearby wavelengths in the infrared with narrow line-width enables the assessment of SpO2 without calibration. [ABSTRACT FROM AUTHOR]

Arterial oxygen saturation (SaO2) and partial arterial pressure of carbon dioxide (PaCO2) are important respiratory parameters in critically ill neonates. A sensor combining a pulse oximeter with the Stow-Severinghaus electrode, required for the measurement of peripheral oxygen saturation (SpO2) and transcutaneous partial pressure of carbon dioxide (PtcCO2), respectively, has been recently used in neonatal clinical practice (TOSCA500®Radiometer). We evaluated TOSCA usability and reliability in the delivery room (DR), throughout three different periods, on term, late-preterm, and preterm neonates. During the first period (period A), 30 healthy term neonates were simultaneously monitored with both TOSCA and a MASIMO pulse oximeter. During the second period (period B), 10 healthy late-preterm neonates were monitored with both TOSCA and a transcutaneous device measuring PtcCO2 (TINA® TCM3, Radiometer). During the third period (period C), 15 preterm neonates were monitored with TOSCA and MASIMO after birth, during stabilization, and during transport to the neonatal intensive care unit (NICU). Blood gas analyses were performed to compare transcutaneous and blood gas values. TOSCA resulted easily and safely usable in the DR, allowing reliable noninvasive SaO2 estimation. Since PtcCO2 measurements with TOSCA required at least 10 min to be stable and reliable, this parameter was not useful during the early resuscitation immediately after birth. Moreover, PtcCO2 levels were less precise if compared to the conventional transcutaneous monitoring. However, PtcCO2 measurement by TOSCA was useful as trend-monitoring after stabilization and during transport to NICU. [ABSTRACT FROM AUTHOR]

The article discusses the impact of various modes of ventilation on the pulse oximeter waveform. It explores the application of modern digital signal processing in assessing the response of the waveform. It aims to encourage the development of methods of patient monitoring through the understanding of the underlying physiology.

This paper describes a three-layer head phantom with artificial pulsating arteries at five different depths (1.2 mm, 3.7 mm, 6.8 mm, 9.6 mm and 11.8 mm). The structure enables formation of spatially and temporally varying tissue properties similar to those of living tissues. In our experiment, pressure pulses were generated in the arteries by an electronically controlled pump. The physical and optical parameters of the layers and the liquid in the artificial arteries were similar to those of real tissues and blood. The amplitude of the pulsating component of the light returning from the phantom tissues was measured at each artery depth mentioned above. The build-up of the in-house-developed pulse oximeter used for performing the measurements and the physical layout of the measuring head are described. The radiant flux generated by the LED on the measuring head was measured to be 1.8 mW at 910 nm. The backscattered radiant flux was measured, and found to be 0.46 nW (0.26 ppm), 0.55 nW (0.31 ppm), and 0.18 nW (0.10 ppm) for the 1.2 mm, 3.7 mm and 6.8 mm arteries, respectively. In the case of the 9.6 mm and 11.8 mm arteries, useful measurement data were not obtained owing to weak signals. We simulated the phantom with the arteries at the above-mentioned five depths and at two additional ones (2.5 mm and 5.3 mm in depth) using the Monte Carlo method. The measurement results were verified by the simulation results. We concluded that in case of 11 mm source-detector separation the arteries at a depth of about 2.5 mm generate the strongest pulse oximeter signal level in a tissue system comprising three layers of thicknesses: 1.5 mm (skin), 5.0 mm (skull), and >50 mm (brain). [ABSTRACT FROM AUTHOR]

Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human's voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors' knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition. [ABSTRACT FROM AUTHOR]

Oxygen saturation (SPO2) is an important indicator of health, and is usually measured by placing a pulse oximeter in contact with a finger or earlobe. However, this method has a problem in that the skin and the sensor must be in contact, and an additional light source is required. To solve these problems, we propose a non-contact oxygen saturation measurement technique that uses a single RGB camera in an ambient light environment. Utilizing the fact that oxygenated and deoxygenated hemoglobin have opposite absorption coefficients at green and red wavelengths, the color space of photoplethysmographic (PPG) signals recorded from the faces of study participants were converted to the YCgCr color space. Substituting the peaks and valleys extracted from the converted Cg and Cr PPG signals into the Beer–Lambert law yields the SPO2 via a linear equation. When the non-contact SPO2 measurement value was evaluated based on the reference SPO2 measured with a pulse oximeter, the mean absolute error was 0.537, the root mean square error was 0.692, the Pearson correlation coefficient was 0.86, the cosine similarity was 0.99, and the intraclass correlation coefficient was 0.922. These results confirm the feasibility of non-contact SPO2 measurements. [ABSTRACT FROM AUTHOR]

Background: Finger pulse oximeters are widely used to monitor physiological responses to high-altitude exposure, the progress of acclimatization, and/or the potential development of high-altitude related diseases. Although there is increasing evidence for its invaluable support at high altitude, some controversy remains, largely due to differences in individual preconditions, evaluation purposes, measurement methods, the use of different devices, and the lacking ability to interpret data correctly. Therefore, this review is aimed at providing information on the functioning of pulse oximeters, appropriate measurement methods and published time courses of pulse oximetry data (peripheral oxygen saturation, (SpO2) and heart rate (HR), recorded at rest and submaximal exercise during exposure to various altitudes. Results: The presented findings from the literature review confirm rather large variations of pulse oximetry measures (SpO2 and HR) during acute exposure and acclimatization to high altitude, related to the varying conditions between studies mentioned above. It turned out that particularly SpO2 levels decrease with acute altitude/hypoxia exposure and partly recover during acclimatization, with an opposite trend of HR. Moreover, the development of acute mountain sickness (AMS) was consistently associated with lower SpO2 values compared to individuals free from AMS. Conclusions: The use of finger pulse oximetry at high altitude is considered as a valuable tool in the evaluation of individual acclimatization to high altitude but also to monitor AMS progression and treatment efficacy. [ABSTRACT FROM AUTHOR]

The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates. [ABSTRACT FROM AUTHOR]

Adequate oxygen delivery to a tissue depends on sufficient oxygen content in arterial blood and blood flow to the tissue. Oximetry is a technique for the assessment of blood oxygenation by measurements of light transmission through the blood, which is based on the different absorption spectra of oxygenated and deoxygenated hemoglobin. Oxygen saturation in arterial blood provides information on the adequacy of respiration and is routinely measured in clinical settings, utilizing pulse oximetry. Oxygen saturation, in venous blood (SvO2) and in the entire blood in a tissue (StO2), is related to the blood supply to the tissue, and several oximetric techniques have been developed for their assessment. SvO2 can be measured non-invasively in the fingers, making use of modified pulse oximetry, and in the retina, using the modified Beer–Lambert Law. StO2 is measured in peripheral muscle and cerebral tissue by means of various modes of near infrared spectroscopy (NIRS), utilizing the relative transparency of infrared light in muscle and cerebral tissue. The primary problem of oximetry is the discrimination between absorption by hemoglobin and scattering by tissue elements in the attenuation measurement, and the various techniques developed for isolating the absorption effect are presented in the current review, with their limitations. [ABSTRACT FROM AUTHOR]

We present a novel smartwatch, CareUp ® , for estimating the Blood Pressure (BP) in real time. It consists of two pulse oximeters: one placed on the back and one on the front of the device. Placing the index finger on the front oximeter starts the acquisition of two photoplethysmograms (PPG); the signals are then filtered and cross-correlated to obtain a Time Delay between them, called Pulse Transit Time (PTT). The Heart Rate (HR) (estimated from the finger PPG) and the PTT are then input in a linear model to give an estimation of the Systolic and Diastolic BP. The performance of the smartwatch in measuring BP have been validated in the Institut Coeur Paris Centre Turin (ICPC), using a sphygmomanometer, on 44 subjects. During the validation, the measures of the CareUp ® were compared to those of two oscillometry-based devices already available on the market: Thuasne ® and Magnien ® . The results showed an accuracy comparable to the oscillometry-based devices and they almost agreed with the American Association for the Advancement of Medical Instrumentation standard for non-automated sphygmomanometers. The integration of the BP estimation algorithm in the smartwatch makes the CareUp ® an easy-to-use, wearable device for monitoring the BP in real time. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) based pulse oximetry devices normally use red and infrared illuminations to obtain oxygen saturation (SpO 2 ) readings. In addition, the presence of motion artefacts severely restricts the utility of pulse oximetry physiological measurements. In the current study, a combination of green and orange illuminations from a multi-wavelength optoelectronic patch sensor (mOEPS) was investigated in order to improve robustness to subjects' movements in the extraction of SpO 2 measurement. The experimental protocol with 31 healthy subjects was divided into two sub-protocols, and was designed to determine SpO 2 measurement. The datasets for the first sub-protocol were collected from 15 subjects at rest, with the subjects free to move their hands. The datasets for the second sub-protocol with 16 subjects were collected during cycling and walking exercises. The results showed good agreement with SpO 2 measurements (r = 0.98) in both sub-protocols. The outcomes promise a robust and cost-effective approach of physiological monitoring with the prospect of providing health monitoring that does not restrict user physical movements. [ABSTRACT FROM AUTHOR]

Oxygen saturation in arterial blood (SaO2) provides information about the performance of the respiratory system. Non-invasive measurement of SaO2 by commercial pulse oximeters (SpO2) make use of photoplethysmographic pulses in the red and infrared regions and utilizes the different spectra of light absorption by oxygenated and de-oxygenated hemoglobin. Because light scattering and optical path-lengths differ between the two wavelengths, commercial pulse oximeters require empirical calibration which is based on SaO2 measurement in extracted arterial blood. They are still prone to error, because the path-lengths difference between the two wavelengths varies among different subjects. We have developed modified pulse oximetry, which makes use of two nearby infrared wavelengths that have relatively similar scattering constants and path-lengths and does not require an invasive calibration step. In measurements performed on adults during breath holding, the two-infrared pulse oximeter and a commercial pulse oximeter showed similar changes in SpO2. The two pulse oximeters showed similar accuracy when compared to SaO2 measurement in extracted arterial blood (the gold standard) performed in intensive care units on newborns and children with an arterial line. Errors in SpO2 because of variability in path-lengths difference between the two wavelengths are expected to be smaller in the two-infrared pulse oximeter. [ABSTRACT FROM AUTHOR]