Heart-rate tuned comb filters for processing photoplethysmogram (PPG) signals in pulse oximetry.

Calculation of peripheral capillary oxygen saturation [Formula: see text] levels in humans is often made with a pulse oximeter, using photoplethysmography (PPG) waveforms. However, measurements of PPG waveforms are susceptible to motion noise due to subject and sensor movements. In this study, we compare two [Formula: see text]-level calculation techniques, and measure the effect of pre-filtering by a heart-rate tuned comb peak filter on their performance. These techniques are: (1) "Red over Infrared," calculating the ratios of AC and DC components of the red and infrared PPG signals,[Formula: see text], followed by the use of a calibration curve to determine the [Formula: see text] level Webster (in: Design of pulse oximeters, CRC Press, Boca Raton, 1997); and (2) a motion-resistant algorithm which uses the Discrete Saturation Transform (DST) (Goldman in J Clin Monit Comput 16:475-83, 2000). The DST algorithm isolates individual "saturation components" in the optical pathway, which allows separation of components corresponding to the [Formula: see text] level from components corresponding to noise and interference, including motion artifacts. The comparison we provide here (employing the two techniques with and without pre-filtering) addresses two aspects: (1) accuracy of the [Formula: see text] calculations; and (2) computational complexity. We used both synthetic data and experimental data collected from human subjects. The human subjects were tested at rest and while exercising; while exercising, their measurements were subject to the impacts of motion. Our main conclusion is that if an uninterrupted high-quality heart rate measurement is available, then the "Red over Infrared" approach preceded by a heart-rate tuned comb filter provides the preferred trade-off between [Formula: see text]-level accuracy and computational complexity. A modest improvement in [Formula: see text] estimate accuracy at very low SNR environments may be achieved by switching to the pre-filtered DST-based algorithm (up to 6% improvement in [Formula: see text] level accuracy at -10 dB over unfiltered DST algorithm and the filtered "Red over Infrared" approach). However, this improvement comes at a significant computational cost.

Correlation of Cognitive Scores and the Onset of Hypoxia.

INTRODUCTION: The negative effects of hypoxia on human cognitive function have been well documented. In this study we assess the correlation of performance in the SynWin cognitive Multi-Task Battery (MTB) and the onset of hypoxia and describe the use of cognitive assessment scores for real-time hypoxia detection.METHODS: We performed a correlation analysis between MTB scores (Arithmetic, Memory, Audio Monitoring, Video Monitoring tasks) and blood oxygen saturation levels to discover if the scores are good candidates to detect hypoxia. Since this analysis showed positive correlation, we proceeded to develop a parallel decision fusion system that uses these cognitive scores for real-time hypoxia detection using the Neyman-Pearson criterion.RESULTS: We demonstrate that MTB scores have considerable hypoxia detection potential and can be used (if measurable passively) in a real-time detection framework. Analysis of receiver operating characteristic (ROC) curves established a hierarchy of importance of the various MTB modules. The Arithmetic task module had the most significant contribution toward correct hypoxia detection (improvement of ∼13.5% and ∼13.9% in detection accuracy under global false alarms of 0.1 and 0.05, respectively), followed by the Memory and Audio Monitoring modules. Fusion of multiple cognitive assessment scores resulted in significantly higher detection accuracy (>86%) than using any one of the scores by itself.DISCUSSION: When available, cognitive assessment scores can be a useful tool for real-time hypoxia detection. Since these MTB tests also assess neuropsychological functioning, study of distributed detection systems based on MTB scores could help in designing tests that are more useful for detecting hypoxic symptoms.Rajasekar A, Acharya S, Shender BS, Rorres C, Hrebien L, Kam M. Correlation of cognitive scores and the onset of hypoxia. Aerosp Med Hum Perform. 2019; 90(5):429-439.

Real-Time Hypoxia Prediction Using Decision Fusion.

Humans who operate in high altitudes for prolonged durations often suffer from hypoxia. The commencement of physiological and cognitive changes due to the onset of hypoxia may not be immediately apparent to the exposed individual. These changes can go unrecognized for minutes and even hours and may lead to serious performance degradation or complete incapacitation. A dynamic system capable of monitoring and detecting decreased physiologic states due to the onset of hypoxia has the potential to prevent adverse outcomes. In this study, we develop a real-time hypoxia monitoring system based on a parallel M -ary decision fusion architecture. Blood oxygen saturation levels and altitude readings are the inputs and estimates of the level of hypoxia are the outputs. We develop new temporal evolution models for blood oxygen saturation and functional impairment with respect to varying altitude. The proposed models enable accurate tracking of various hypoxia levels based on the duration of stay of the subject at an altitude. Using a Bayesian decision-making formulation, the system generates global estimates of the degree of hypoxia. The detection system is tested against synthetic and real datasets to demonstrate applicability and accuracy.

Analysis of cytokine release assay data using machine learning approaches.

The possible onset of Cytokine Release Syndrome (CRS) is an important consideration in the development of monoclonal antibody (mAb) therapeutics. In this study, several machine learning approaches are used to analyze CRS data. The analyzed data come from a human blood in vitro assay which was used to assess the potential of mAb-based therapeutics to produce cytokine release similar to that induced by Anti-CD28 superagonistic (Anti-CD28 SA) mAbs. The data contain 7 mAbs and two negative controls, a total of 423 samples coming from 44 donors. Three (3) machine learning approaches were applied in combination to observations obtained from that assay, namely (i) Hierarchical Cluster Analysis (HCA); (ii) Principal Component Analysis (PCA) followed by K-means clustering; and (iii) Decision Tree Classification (DTC). All three approaches were able to identify the treatment that caused the most severe cytokine response. HCA was able to provide information about the expected number of clusters in the data. PCA coupled with K-means clustering allowed classification of treatments sample by sample, and visualizing clusters of treatments. DTC models showed the relative importance of various cytokines such as IFN-γ, TNF-α and IL-10 to CRS. The use of these approaches in tandem provides better selection of parameters for one method based on outcomes from another, and an overall improved analysis of the data through complementary approaches. Moreover, the DTC analysis showed in addition that IL-17 may be correlated with CRS reactions, although this correlation has not yet been corroborated in the literature.

Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow.

Analysis of multicolor flow cytometric data is traditionally based on the judgment of an expert, generally time consuming, sometimes incomplete and often subjective in nature. In this article, we investigate another statistical method using a Sequential Univariate Gating (SUG) algorithm to identify regions of interest between two groups of multivariate flow cytometric data. The metric used to differentiate between the groups of univariate distributions in SUG is the Kolmogorov-Smirnov distance (D) statistic. The performance of the algorithm is evaluated by applying it to a known three-color data set looking at activation of CD4+ and CD8+ lymphocytes with anti-CD3 antibody treatment and comparing the results to the expert analysis. The algorithm is then applied to a four-color data set used to study the effects of recombinant human erythropoietin (rHuEPO) on several murine bone marrow populations. SUG was used to identify regions of interest in the data and results compared to expert analysis and the current state-of-the-art statistical method, Frequency Difference Gating (FDG). Cluster analysis was then performed to identify subpopulations responding differently to rHuEPO. Expert analysis, SUG and FDG identified regions in the data that showed activation of CD4+ and CD8+ lymphocytes with anti-CD3 treatment. In the rHuEPO treated data sets, the expert and SUG identified a dose responsive expansion of only the erythroid precursor population. In contrast, FDG resulted in identification of regions of interest both in the erythroid precursors as well as in other bone marrow populations. Clustering within the regions of interest defined by SUG resulted in identification of four subpopulations of erythroid precursors that are morphologically distinct and show a differential response to rHuEPO treatment. Greatest expansion is seen in the basophilic and poly/orthochromic erythroblast populations with treatment. Identification of populations of interest can be performed using SUG in less subjective, time efficient, biologically interpretable manner that corroborates with the expert analysis. The results suggest that basophilic erythroblasts cells or their immediate precursors are an important target for the effects of rHuEPO in murine bone marrow. The MATLAB implementation of the method described in the article, both experimental data and other supplemental materials are freely available at http://web.mac.com/acidrap18.

A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow.

BACKGROUND: Cellular binding of annexin V and membrane permeability to 7-aminoactinomycin D (7AAD) are important tools for studying apoptosis and cell death by flow cytometry. Combining viability markers with cell surface marker expression is routinely used to study various cell lineages. Current classification methods using strict thresholds, or "gates," on the fluorescent intensity of these markers are subjective in nature and may not fully describe the phenotypes of interest. We have developed objective criteria for phenotypic boundary recognition through the application of statistical pattern recognition. This task was achieved using artificial neural networks (ANNs) that were trained to recognize subsets of cells with known phenotypes, and then used to determine decision boundaries based on statistical measures of similarity. This approach was then used to test the hypothesis that erythropoietin (EPO) inhibits apoptosis and cell death in erythroid precursor cells in murine bone marrow. METHODS: Our method was developed for classification of viability using an in vitro cell system and then applied to an ex vivo analysis of murine late-stage erythroid progenitors. To induce apoptosis and cell death in vitro, an EPO-dependent human leukemic cell line, UT-7(EPO) cells were incubated without recombinant human erythropoietin (rhEPO) for 72 h. Five different ANNs were trained to recognize live, apoptotic, and dead cells using a "known" subset of the data for training, and a K-fold cross validation procedure for error estimation. The ANNs developed with the in vitro system were then applied to classify cells from an ex vivo study of rhEPO treated mice. Tg197 (human tumor necrosis-alpha transgenic mice, a model of anemia of chronic disease) received a single s.c. dose of 10,000 U/kg rhEPO and femoral bone marrow was collected 1, 2, 4, and 8 days after dosing. Femoral bone marrow cells were stained with TER-119 PE, CD71 APC enable identification of erythroid precursors, and annexin V FITC and 7AAD to identify the apoptotic and dead cells. During classification forward and side angle light scatter were also input to all pattern recognition systems. RESULTS: Similar decision boundaries between live, apoptotic, and dead cells were consistently identified by the neural networks. The best performing network was a radial basis function multi-perceptron that produced an estimated average error rate of 4.5% +/- 0.9%. Using these boundaries, the following results were reached: depriving UT-7(EPO) cells of rhEPO induced apoptosis and cell death while the addition of rhEPO rescued the cells in a dose-dependent manner. In vivo, treatment with rhEPO resulted in an increase of live erythroid cells in the bone marrow to 119.8% +/- 9.8% of control at the 8 day time point. However, a statistically significant transient increase in TER-119(+) CD71(+) 7AAD(+) dead erythroid precursors was observed at the 1 and 2 day time points with a corresponding decrease in TER-119(+) CD71(+) 7AAD(-) Annexin V(-) live erythroid precursors, and no change in the number of TER-119(+) CD71(+) annexin V(+) 7AAD(-) apoptotic erythroid precursors in the bone marrow. CONCLUSIONS: A statistical pattern recognition approach to viability classification provides an objective rationale for setting decision boundaries between "positive" and "negative" intensity measures in cytometric data. Using this approach we have confirmed that rhEPO inhibits apoptosis and cell death in an EPO dependent cell line in vitro, but failed to do so in vivo, suggesting EPO may not act as a simple antiapoptotic agent in the bone marrow. Rather, homeostatic mechanisms may regulate the pharmacodynamic response to rhEPO.

New criteria for selecting differentially expressed genes.

One of the major concerns in detecting changes in higher moments is these changes may be due to outliers or process errors that are not biologically significant. For example, a larger variance observed in the expression levels may simply due to the larger variation in the data collecting process. Several outliers, which exhibit some extreme expression levels than the rest of the samples, may also increase the variance or skewness of the expression levels significantly. So it is very important to reduce the effect of outliers and process errors by proper experimental designs [27], such as technical replicates and biological replicates, before high sensitivity criterion, such as ADS, can be applied. We have presented and demonstrated the operation of two new criteria, ADS and the MDS, for identifying differentially expressed genes. These two criteria were compared with several commonly used criteria, namely WTS, WRS, FCS, and ICE. Experiments with simulated data show ADS to be more powerful than the WTS. When high-sensitivity screening is required, ADS appears to be preferable to WTS. When an FPR similar to WTS is desired, MDS should be used. The popular Wilcoxon rank sum is a more conservative approach that should be employed when the lowest FPR is desired, even at the expense of lower TPRs. ICE is a less desirable criterion because it does not perform well for data generated by the normal model. FCS gave results similar to those of WTS. Evaluation of these algorithms using real biological datasets showed that ADS and MDS flagged several biologically significant genes that were missed by WTS, besides selecting most of the genes that are also selected by WTS.

Consciousness monitoring using near-infrared spectroscopy (NIRS) during high +Gz exposures.

The relationship between human consciousness and oxygen saturation (rSO(2)) in cerebral tissue under high +Gz stress was non-invasively monitored by near-infrared multiple wavelength spectroscopy (NIRS). We studied the drop in rSO(2) levels in human subjects during exposure to various head-to-foot acceleration (+Gz) profiles. These profiles included sustained +Gz plateaus and repeated short duration +Gz pulses of varying duration. The end point in this study was +Gz-induced loss of consciousness (G-LOC). The rSO(2) levels under normal (asymptomatic), almost loss of consciousness (A-LOC) and G-LOC conditions were recorded. Correlations among decrease in rSO(2), +Gz pulse duration, +Gz stress level and incapacitation time (ICAP) after G-LOC were also investigated. It was found that once rSO(2) fell to a certain level, G-LOC occurred. This threshold was repeatable and independent of the +Gz level or duration. It was also observed that the total ICAP after G-LOC was dependent on the length of time that rSO(2) remained below the G-LOC threshold level, i.e. the longer the rSO(2) level remained below the G-LOC induction level, the longer the subject remained unconscious. These results may prove to be useful in designing closed loop control systems for personal protective gear for pilots of high performance aircraft.

Acceleration-induced near-loss of consciousness: the "A-LOC" syndrome.

BACKGROUND: There is an insidious phenomenon that can occur when aircrew are exposed to +Gz stress even at levels that are insufficient to cause +Gz-induced loss of consciousness (G-LOC). Under these circumstances aircrew exhibit an altered state of awareness that was termed Almost Loss of Consciousness (A-LOC) by the U.S. Navy in the late 1980's. A-LOC is a syndrome that includes a wide variety of cognitive, physical, emotional, and physiological symptoms. While A-LOC has been observed in centrifuge studies and reported in flight for over 15 yr, a definitive description of the syndrome does not exist. METHODS: Nine subjects were exposed to short +6, 8, and 10 Gz pulses of increasing duration until they experienced G-LOC. Instrumentation included two channels of ECG and near infrared spectroscopy (NIRS) to measure relative cerebral tissue oxygenation (rSo2). Subjects indicated +Gz-induced visual symptoms (light loss, LL) by pressing a switch when LL began and releasing it when total vision was restored. Short-term memory loss was assessed using a simple math task. Data analysis included a description and the time course of the physical, physiological, cognitive, and emotional responses. RESULTS: There were 66 episodes of A-LOC that were identified out of a total of 161 +Gz pulse exposures. Many incidents of sensory abnormalities, amnesia, confusion, euphoria, difficulty in forming words, and reduced auditory acuity were documented. Often these responses occurred in multiple subjects and at different +Gz levels. One of the most common symptoms was a disconnection between cognition and the ability to act on it. There was a significant reduction in rSo2 over baseline, greater overshoot in rSo2 (increase in oxygenation above baseline after the +Gz exposure), faster fall in rSo2 during +Gz stress, and prolonged recovery time associated with A-LOC as compared with +Gz exposures without symptoms. CONCLUSION: Evaluation of the range of symptoms associated with A-LOC can lead to a program to increase pilots' awareness of the phenomenon and further our understanding of the relationship between the outward symptoms and the underlying physiological changes.

Noninvasive monitoring of human consciousness by near-infrared spectroscopy (NIRS) during high +Gz stress.

Noninvasive monitoring of the relative change in oxygen saturation (rSO2) in cerebral tissue by near-infrared multi-wavelength spectroscopy (NIRS) was investigated in humans under high acceleration (+Gz) stress. These profiles included sustained 15-second +Gz plateaus and repeated short duration +Gz pulses of varying duration. The end points in this study were loss of consciousness due to high +Gz exposure (GLOC). In many cases subjects demonstrated cognitive and physical symptoms related to reduced cerebral blood flow without frank unconscious, which has been called Almost Loss of Consciousness (ALOC). Both the rSO2 levels during and after the +Gz exposures and the total time subjects were incapacitated after GLOC were recorded. It was found that while the drop in rSO2 at the onset of GLOC was lower during pulse exposures as compared to sustained exposures, the total time to recovery from GLOC was longer during the sustained runs. By applying a better understanding of the nature and timing of +Gz-induced changes in cerebral tissue oxygenation, more efficient control systems for personal protective gear for pilots of high performance aircraft can be implemented.