Multi-channel pulse oximetry for wearable physiological monitoring

Mendelson, Yitzhak; Dao, Duy; Chon, Ki H.

doi:10.1109/bsn.2013.6575518

Cited by 20 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aiming to mitigate the effects of motion artefacts, Yan and Zhang developed and algorithm using a minimum correlation discrete saturation transform to estimate SpO2, which can achieve a better performance than the clinically verified motion-resistant algorithmdiscrete saturate transformwhen signal quality is low [28]. Mendelson et al investigated a multichannel reflectance pulse oximeter that proved to be efficient for robust noise cancellation with PPG signal acquired concurrently from each channel [29]. Chacon et al reported a wireless wearable pulse oximeter that was integrated with a novel data-dependent motion artefact tailoring algorithm, which demonstrated to be an efficient method for continuous monitoring of SpO2 [30].…”

Section: A Oxygen Saturationmentioning

confidence: 99%

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

207

155

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death.A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

show abstract

Section: A Oxygen Saturationmentioning

confidence: 99%

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Ding

Clifton

et al. 2021

IEEE Rev. Biomed. Eng.

207

155

View full text Add to dashboard Cite

show abstract

“…Four types of non-invasive multichannel POs (MCPOs) that were developed in our lab [ 14 ] were used on trauma patients: 1) a 6PD (photo detector) forehead MCPO providing 6 PPG channels; 2) a 3LED (light emitting diode) forehead MCPO providing 3 PPG channels; 3) a 3PD ear MCPO; and 4) a 3PD finger MCPO, both providing 3 PPG channels. Note that the two forehead MCPOs were not used at the same time, and only three MCPOs were recording simultaneously on different locations on the skin.…”

Section: Methodsmentioning

confidence: 99%

“…The encouraging results from studies previously performed in our group [ 8 ] and [ 9 ] have led us to the idea of addressing the problem of blood loss detection in the intraoperative and trauma care settings. Therefore, the aim of this study was to automatically classify blood loss from PPG recordings acquired with portable, non-invasive POs developed in our lab [ 14 ] from trauma patients admitted to the UMass Medical Center as early as possible upon their arrival, and from healthy volunteers subjected to 0.9 L blood withdrawal using commercially available POs. We characterized every PPG recording with a feature vector that has, to the best of our knowledge, not been previously used by other researchers, consisting of four features: the goodness of fit ( r 2 ), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points.…”

Section: Introductionmentioning

confidence: 99%

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

et al. 2018

View full text Add to dashboard Cite

Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r2), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.

show abstract

“…A summary of the indices of EDA and PRV computed in this study is included in Table 2. [18][19][20], with the advantage that photoplethysmography is a low-cost optical technique [21] that is much simpler to use than the electrocardiogram and is easily deployed in smartphones and wearable devices [22][23][24]. For PRV analysis, four minutes of clean PPG signals were extracted from the rest and test stages of the Stroop task.…”

Section: Data Processingmentioning

confidence: 99%

Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress

et al. 2019

View full text Add to dashboard Cite

The feasibility of detecting mild dehydration by using autonomic responses to cognitive stress was studied. To induce cognitive stress, subjects (n = 17) performed the Stroop task, which comprised four minutes of rest and four minutes of test. Nine indices of autonomic control based on electrodermal activity (EDA) and pulse rate variability (PRV) were obtained during both the rest and test stages of the Stroop task. Measurements were taken on three consecutive days in which subjects were "wet" (not dehydrated) and "dry" (experiencing mild dehydration caused by fluid restriction). Nine approaches were tested for classification of "wet" and "dry" conditions:(1) linear (LDA) and (2) quadratic discriminant analysis (QDA), (3) logistic regression, (4) support vector machines (SVM) with cubic, (5) fine Gaussian kernel, (6) medium Gaussian kernel, (7) a k-nearest neighbor (KNN) classifier, (8) decision trees, and (9) subspace ensemble of KNN classifiers (SE-KNN). The classification models were tested for all possible combinations of the nine indices of autonomic nervous system control, and their performance was assessed by using leave-one-subject-out cross-validation. An overall accuracy of mild dehydration detection was 91.2% when using the cubic SE-KNN and indices obtained only at rest, and the accuracy was 91.2% when using the cubic SVM classifiers and indices obtained only at test. Accuracy was 86.8% when rest-to-test increments in the autonomic indices were used along with the KNN and QDA classifiers. In summary, measures of autonomic function based on EDA and PRV are suitable for detecting mild dehydration and could potentially be used for the noninvasive testing of dehydration.

show abstract

Multi-channel pulse oximetry for wearable physiological monitoring

Cited by 20 publications

References 18 publications

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia

Mild Dehydration Identification Using Machine Learning to Assess Autonomic Responses to Cognitive Stress

Contact Info

Product

Resources

About