Detection of hypoxia by near-infrared spectroscopy and pulse oximetry: a comparative study

Noninvasive cardiac hemodynamics monitoring of acute myocardial ischemia in rats using near-infrared spectroscopy: A pilot study

Noninvasive and real-time optical detection of cardiac hemodynamics dysfunction during myocardial ischemia remains challenging. In this study, we developed a near-infrared spectroscopy device to monitor rats' myocardial hemodynamics. The well-designed system can accurately reflect the hemodynamics changes by the classic upper limb ischemia test. Systemic hypoxia by disconnecting to the ventilator and cardiac ischemia by coronary artery slipknot ligation was conducted to monitor myocardial hemodynamics. When systemic hypoxia occurred, ΔHbR and ΔtHb increased significantly, whereas ΔHbO decreased rapidly. When coronary blood flow was obstructed by slipknots, cardiothoracic ΔHbO immediately begins to decline, while ΔHbR also significantly increases. Simultaneously, SpO2 did not show any obvious changes during myocardial ischemia, while SpO2 decreased significantly during systemic hypoxia. These results demonstrated that cardiothoracic hemodynamics stemmed from myocardial ischemia. This pilot study demonstrated the practicality of noninvasive, low-cost optical monitoring for cardiac oxygenation dysfunction in rats.

History and Social Implications of the Pulse Oximeter

The pulse oximeter is a portable, bedside tool that allows for the measurement of oxygen saturation in a patient's red blood cells. The technology is based on oxygenated and deoxygenated hemoglobin absorbing light at different wavelengths. The device calculates the ratio of oxygenated to deoxygenated hemoglobin in the blood, and an algorithm produces a percentage oxygen saturation value. Due to its portability and ease of use, it is a ubiquitous medical tool that is commonly used in medical practice. This paper reviews the history and evolution of this tool, and the scientific laws behind oximetry. It also introduces the importance of the pulse oximeter and its basic functions. In addition, the limitations of pulse oximetry are discussed, especially as they pertain to pigmented skin.

Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients

A two-stream convolutional neural network (TCNN) for breathing pattern classification has been devised for the continuous monitoring of patients with infectious respiratory diseases. The TCNN consists of a convolutional neural network (CNN)-based autoencoder and classifier. The encoder of the autoencoder generates deep compressed feature maps, which contain the most important information constituting data. These maps are concatenated with feature maps generated by the classifier to classify breathing patterns. The TCNN, single-stream CNN (SCNN), and state-of-the-art classification models were applied to classify four breathing patterns: normal, slow, rapid, and breath holding. The input data consisted of chest tissue hemodynamic responses measured using a wearable near-infrared spectroscopy device on 14 healthy adult participants. Among the classification models evaluated, random forest had the lowest classification accuracy at 88.49%, while the TCNN achieved the highest classification accuracy at 94.63%. In addition, the proposed TCNN performed 2.6% better in terms of classification accuracy than an SCNN (without an autoencoder). Moreover, the TCNN mitigates the issue of declining learning performance with increasing network depth, as observed in the SCNN model. These results prove the robustness of the TCNN in classifying breathing patterns despite using a significantly smaller number of parameters and computations compared to state-of-the-art classification models.

Oxygen Sensor-Guided Fine Needle Biopsy Studies of Human Cancer Xenografts in Mice

An oxygen sensor-mounted fine-needle biopsy tool was used for in vivo measurement of oxygen levels in tumor xenografts. The system provides a means of measuring the oxygen content in harvested tumor tissue from specific locations. Oxygen in human tumor xenografts in a murine model was observed for over 1 min. Tissues were mapped in relation to oxygen tension (pO2) readings and sampled for conventional cytological examination. Careful modeling of the pO2 readings over 60 seconds yielded a diffusion coefficient for oxygen at the sensor tip, providing additional diagnostic information about the tissue before sampling. Oxygen level measurement may provide a useful adjunct to the use of biomarkers in tumor diagnosis.

Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review

Out-of-hospital cardiac arrest (OHCA) is a major health problem, with a poor survival rate of 2-11%. For the roughly 75% of OHCAs that are unwitnessed, survival is approximately 2-4.4%, as there are no bystanders present to provide life-saving interventions and alert Emergency Medical Services. Sensor technologies may reduce the number of unwitnessed OHCAs through automated detection of OHCA-associated physiological changes. However, no technologies are widely available for OHCA detection. This review identifies research and commercial technologies developed for cardiopulmonary monitoring that may be best suited for use in the context of OHCA, and provides recommendations for technology development, testing, and implementation. We conducted a systematic review of published studies along with a search of grey literature to identify technologies that were able to provide cardiopulmonary monitoring, and could be used to detect OHCA. We searched MEDLINE, EMBASE, Web of Science, and Engineering Village using MeSH keywords. Following inclusion, we summarized trends and findings from included studies. Our searches retrieved 6945 unique publications between January, 1950 and May, 2023. 90 studies met the inclusion criteria. In addition, our grey literature search identified 26 commercial technologies. Among included technologies, 52% utilized electrocardiography (ECG) and 40% utilized photoplethysmography (PPG) sensors. Most wearable devices were multi-modal (59%), utilizing more than one sensor simultaneously. Most included devices were wearable technologies (84%), with chest patches (22%), wrist-worn devices (18%), and garments (14%) being the most prevalent. ECG and PPG sensors are heavily utilized in devices for cardiopulmonary monitoring that could be adapted to OHCA detection. Developers seeking to rapidly develop methods for OHCA detection should focus on using ECG- and/or PPG-based multimodal systems as these are most prevalent in existing devices. However, novel sensor technology development could overcome limitations in existing sensors and could serve as potential additions to or replacements for ECG- and PPG-based devices.

Noninvasive Monitoring Strategies for Bronchopulmonary Dysplasia or Post-Prematurity Respiratory Disease: Current Challenges and Future Prospects

Definitions of bronchopulmonary dysplasia (BPD) or post-prematurity respiratory disease (PPRD) aim to stratify the risk of mortality and morbidity, with an emphasis on long-term respiratory outcomes. There is no univocal classification of BPD due to its complex multifactorial nature and the substantial heterogeneity of clinical presentation. Currently, there is no definitive treatment available for extremely premature very-low-birth-weight infants with BPD, and challenges in finding targeted preventive therapies persist. However, innovative stem cell-based postnatal therapies targeting BPD-free survival are emerging, which are likely to be offered in the first few days of life to high-risk premature infants. Hence, we need easy-to-use noninvasive tools for a standardized, precise, and reliable BPD assessment at a very early stage, to support clinical decision-making and to predict the response to treatment. In this non-systematic review, we present an overview of strategies for monitoring preterm infants with early and evolving BPD-PPRD, and we make some remarks on future prospects, with a focus on near-infrared spectroscopy (NIRS).

Everything About Pulse Oximetry-Part 2: Clinical Applications, Portable/Wearable Pulse Oximeters, Remote Patient Monitoring, and Recent Advances

Purpose: Pulse oximetry is widely used in healthcare settings for both screening and continuous monitoring. In this article, it was aimed to review some aspects of pulse oximetry including clinical applications, portable devices, and recent advances in detail. Materials and Methods: The international and national reliable sources were used in the literature review for critical data analysis. A total of 31 articles including 19 prospective comparative clinical studies, 9 reviews, 1 meta-analysis, 1 retrospective study, and 1 experimental study were used for preparation of this part of the review. Results: In this part of the article, clinical applications of pulse oximeters, portable/wearable pulse oximeters, remote patient monitoring, and recent advances were all reviewed in detail. Conclusion: Pulse oximetry is a widely used and reliable noninvasive technique that provides useful information about blood oxygenation in individuals. This technique can guide oxygen therapy, reduce the occurrence of hypoxemia, and decrease the frequency of admissions to the intensive care unit, as well as arterial blood gas sampling. New multiwaveform sensors and advanced signal processing techniques can differentiate between different types of hemoglobin and may be useful for continuous measurement of total hemoglobin, as well as for detecting and providing information on blood loss and cardiac output.

Modification of a Conventional Deep Learning Model to Classify Simulated Breathing Patterns: A Step toward Real-Time Monitoring of Patients with Respiratory Infectious Diseases

The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a need for remote methods to detect and continuously monitor patients with infectious respiratory diseases. Many different devices, including thermometers, pulse oximeters, smartwatches, and rings, were proposed to monitor the symptoms of infected individuals at home. However, these consumer-grade devices are typically not capable of automated monitoring during both day and night. This study aims to develop a method to classify and monitor breathing patterns in real-time using tissue hemodynamic responses and a deep convolutional neural network (CNN)-based classification algorithm. Tissue hemodynamic responses at the sternal manubrium were collected in 21 healthy volunteers using a wearable near-infrared spectroscopy (NIRS) device during three different breathing conditions. We developed a deep CNN-based classification algorithm to classify and monitor breathing patterns in real time. The classification method was designed by improving and modifying the pre-activation residual network (Pre-ResNet) previously developed to classify two-dimensional (2D) images. Three different one-dimensional CNN (1D-CNN) classification models based on Pre-ResNet were developed. By using these models, we were able to obtain an average classification accuracy of 88.79% (without Stage 1 (data size reducing convolutional layer)), 90.58% (with 1 × 3 Stage 1), and 91.77% (with 1 × 5 Stage 1).