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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Autonomic Responses to Microgravity

The purpose of this report is to describe how changes in autonomic nervous system responses may be used as an index of individual differences in adaptational capacity to space flight. During two separate Spacelab missions, six crewmembers wore an ambulatory monitoring system which enabled continuous recording of their physiological responses for up to twelve hours a day for 3 to 5 mission days. The responses recorded were electrocardiography, respiration wave form, skin conductance level, hand temperature, blood flow to the hands and triaxial accelerations of the head and upper body. Three of these subjects had been given training, before the mission, in voluntary control of these autonomic responses as a means of facilitating adaptation to space. Three of these subjects served as Controls, i.e., did not receive this training but took anti-motion sickness medication. Nearly 300 hours of flight data are summarized. These data were examined using time-series analyses, spectral analyses of heart rate variability, and analyses of variance. Information was obtained on responses to space motion sickness, inflight medications, circadian rhythm, workload and fatigue. Preliminary assessment was made on the effectiveness of self-regulation training as a means of facilitating adaptation, with recommendations for future flights.

Toscano, W. B.↗
NASA NTRS1992

Oxygen transport and cardiovascular function at extreme altitude: lessons from Operation Everest II

Operation Everest II was designed to examine the physiological responses to gradual decompression simulating an ascent of Mt Everest (8,848 m) to an inspired PO2 of 43 mmHg. The principal studies conducted were cardiovascular, respiratory, muscular-skeletal and metabolic responses to exercise. Eight healthy males aged 21-31 years began the "ascent" and six successfully reached the "summit", where their resting arterial blood gases were PO2 = 30 mmHg and PCO2 = 11 mmHg, pH = 7.56. Their maximal oxygen uptake decreased from 3.98 +/- 0.2 L/min at sea level to 1.17 +/- 0.08 L/min at PIO2 43 mmHg. The principal factors responsible for oxygen transport from the atmosphere to tissues were (1) Alveolar ventilation--a four fold increase. (2) Diffusion from the alveolus to end capillary blood--unchanged. (3) Cardiac function (assessed by hemodynamics, echocardiography and electrocardiography)--normal--although maximum cardiac output and heart rate were reduced. (4) Oxygen extraction--maximal with PvO2 14.8 +/- 1 mmHg. With increasing altitude maximal blood and muscle lactate progressively declined although at any submaximal intensity blood and muscle lactate was higher at higher altitudes.

Non-NASA Center↗
NASA NTRS2004

Month-to-month and year-to-year reproducibility of high frequency QRS ECG signals

High frequency electrocardiography analyzing the entire QRS complex in the frequency range of 150 to 250 Hz may prove useful in the detection of coronary artery disease, yet the long-term stability of these waveforms has not been fully characterized. Therefore, we prospectively investigated the reproducibility of the root mean squared voltage, kurtosis, and the presence versus absence of reduced amplitude zones in signal averaged 12-lead high frequency QRS recordings acquired in the supine position one month apart in 16 subjects and one year apart in 27 subjects. Reproducibility of root mean squared voltage and kurtosis was excellent over these time intervals in the limb leads, and acceptable in the precordial leads using both the V-lead and CR-lead derivations. The relative error of root mean squared voltage was 12% month-to-month and 16% year-to-year in the serial recordings when averaged over all 12 leads. Reduced amplitude zones were also reproducible up to a rate of 87% and 81%, respectively, for the month-to-month and year-to-year recordings. We conclude that 12-lead high frequency QRS electrocardiograms are sufficiently reproducible for clinical use.

Coronary Disease/diagnosis↗
NASA NTRS2006

Month-to-Month and Year-to-Year Reproducibility of High Frequency QRS ECG signals

High frequency (HF) electrocardiography analyzing the entire QRS complex in the frequency range of 150 to 250 Hz may prove useful in the detection of coronary artery disease, yet the long-term stability of these waveforms has not been fully characterized. We therefore prospectively investigated the reproducibility of the root mean squared (RMS) voltage, kurtosis, and the presence versus absence of reduced amplitude zones (RAzs) in signal averaged 12-lead HF QRS recordings acquired in the supine position one month apart in 16 subjects and one year apart in 27 subjects. Reproducibility of RMS voltage and kurtosis was excellent over these time intervals in the limb leads, and acceptable in the precordial leads using both the V-lead and CR-lead derivations. The relative error of RMS voltage was 12% month-to-month and 16% year-to-year in the serial recordings when averaged over all 12 leads. RAzs were also reproducible at a rate of up to 87% and 8 1 %, respectively, for the month-to-month and year-to-year recordings. We conclude that 12-lead HF QRS electrocardiograms are sufficiently reproducible for clinical use.

Batdorf, Niles↗
NASA NTRS2006

High-frequency ECG

The standard ECG is by convention limited to 0.05-150 Hz, but higher frequencies are also present in the ECG signal. With high-resolution technology, it is possible to record and analyze these higher frequencies. The highest amplitudes of the high-frequency components are found within the QRS complex. In past years, the term "high frequency", "high fidelity", and "wideband electrocardiography" have been used by several investigators to refer to the process of recording ECGs with an extended bandwidth of up to 1000 Hz. Several investigators have tried to analyze HF-QRS with the hope that additional features seen in the QRS complex would provide information enhancing the diagnostic value of the ECG. The development of computerized ECG-recording devices that made it possible to record ECG signals with high resolution in both time and amplitude, as well as better possibilities to store and process the signals digitally, offered new methods for analysis. Different techniques to extract the HF-QRS have been described. Several bandwidths and filter types have been applied for the extraction as well as different signal-averaging techniques for noise reduction. There is no standard method for acquiring and quantifying HF-QRS. The physiological mechanisms underlying HF-QRS are still not fully understood. One theory is that HF-QRS are related to the conduction velocity and the fragmentation of the depolarization wave in the myocardium. In a three-dimensional model of the ventricles with a fractal conduction system it was shown that high numbers of splitting branches are associated with HF-QRS. In this experiment, it was also shown that the changes seen in HF-QRS in patients with myocardial ischemia might be due to the slowing of the conduction velocity in the region of ischemia. This mechanism has been tested by Watanabe et al by infusing sodium channel blockers into the left anterior descending artery in dogs. In their study, 60 unipolar ECGs were recorded from the entire ventricular surface and were signal-averaged and filtered in the 30-250 Hz frequency range. The results showed that the decrease noted in the HF-QRS correlated linearly with the local conduction delay. The results suggest that HF-QRS is a potent indicator of disturbed local conduction. An alternative theory is that HF-QRS reflect the shape of the original electrocardiographic signal. Bennhagen et al showed that root mean square (RMS) voltage values of the depolarization signal correlate poorly with the signal amplitude but highly with the first and second derivatives, i.e. the velocity and the acceleration of the signal. It has also been suggested that the autonomic nervous system affects HF-QRS. For example, sitting up causes significant changes in HF-QRS in some leads compared to the supine position [Douglas et al., 2006]. Unpublished results indicate that familial dysautonomic patients (both vagal and sympathetic degeneration) have very little Reduced Amplitude Zones (RAZ) formation . Athletic individuals, especially elite athletes, who have vagally-mediated changes on the conventional ECG (i.e. early repolarization, bradycardia) have increased RAZ formation. Further electrophysiological studies are needed, however, to better understand the underlying mechanisms of HF-QRS. Several investigators have studied HF-QRS in different cardiac conditions, including acute myocardial ischemia and myocardial infarction (MI). However, in order for clinicians to confidently use HF-QRS as an adjunct to standard ECG, more knowledge about the characteristics of HF-QRS is needed.

Tragardh, Elin↗
NASA NTRS2006

Technological Innovations from NASA

The challenge of human space exploration places demands on technology that push concepts and development to the leading edge. In biotechnology and biomedical equipment development, NASA science has been the seed for numerous innovations, many of which are in the commercial arena. The biotechnology effort has led to rational drug design, analytical equipment, and cell culture and tissue engineering strategies. Biomedical research and development has resulted in medical devices that enable diagnosis and treatment advances. NASA Biomedical developments are exemplified in the new laser light scattering analysis for cataracts, the axial flow left ventricular-assist device, non contact electrocardiography, and the guidance system for LASIK surgery. Many more developments are in progress. NASA will continue to advance technologies, incorporating new approaches from basic and applied research, nanotechnology, computational modeling, and database analyses.

Pellis, Neal R.↗
NASA NTRS2006

User Interactive Software for Analysis of Human Physiological Data

Ambulatory physiological monitoring has been used to study human health and performance in space and in a variety of Earth-based environments (e.g., military aircraft, armored vehicles, small groups in isolation, and patients). Large, multi-channel data files are typically recorded in these environments, and these files often require the removal of contaminated data prior to processing and analyses. Physiological data processing can now be performed with user-friendly, interactive software developed by the Ames Psychophysiology Research Laboratory. This software, which runs on a Windows platform, contains various signal-processing routines for both time- and frequency- domain data analyses (e.g., peak detection, differentiation and integration, digital filtering, adaptive thresholds, Fast Fourier Transform power spectrum, auto-correlation, etc.). Data acquired with any ambulatory monitoring system that provides text or binary file format are easily imported to the processing software. The application provides a graphical user interface where one can manually select and correct data artifacts utilizing linear and zero interpolation and adding trigger points for missed peaks. Block and moving average routines are also provided for data reduction. Processed data in numeric and graphic format can be exported to Excel. This software, PostProc (for post-processing) requires the Dadisp engineering spreadsheet (DSP Development Corp), or equivalent, for implementation. Specific processing routines were written for electrocardiography, electroencephalography, electromyography, blood pressure, skin conductance level, impedance cardiography (cardiac output, stroke volume, thoracic fluid volume), temperature, and respiration

Cowings, Patricia S.↗
NASA NTRS2015

Evaluation of the Accuracy of Astroskin as a Behavioral Health Self-Monitoring System for Spaceflight

In space, there is a need to monitor astronauts' vital signs and assess their readiness to perform specific tasks during a mission. Currently, NASA does not have the capability to noninvasively monitor crew for extended periods of time. The Canadian Space Agency is working with the Psychophysiology Lab at NASA ARC to determine if the Astroskin could be used as a solution to this problem. Astroskin, a commercially available garment with built-in biosensors, can be comfortably worn under clothing or a spacesuit and relay information to the crewman's own mobile device. Data can also be sent wirelessly to the on-board Exploration Medical System. To determine if Astroskin meets requirements for health monitoring, it must first be validated in spaceflight analog environments. In the current study Astroskin data will be compared to traditional biomedical instrument measures of electrocardiography (ECG), respiration rate, and systolic blood pressure. The data will be recorded during Autogenic Feedback Training Exercise (AFTE), which is a type of physiological self-regulation training designed for astronauts. The data will also be recorded during simulations of the Orion spacecraft re-entry. The results to date suggest that Astroskin is a suitable ambulatory monitoring system that allows astronauts to self-diagnose and self-regulate adverse autonomic nervous system responses to sustained exposure to microgravity of spaceflight.

biomedical↗
NASA NTRS2004

Short-Duration Spaceflight Does Not Prolong QTc Intervals in Male Astronauts

Although ventricular dysrhythmias are not increased during, and QTc intervals are not prolonged after, short-duration (5 to 16 days) spaceflights, QTc intervals have not previously been reported during these shorter flights. Holter monitor recordings, obtained in 11 male astronauts who flew on shuttle missions ranging from 5 to 10 days, showed that QTc intervals did not change significantly 10 days before launch, on 2 separate days of spaceflight, and 2 days after landing. Taken together, these data and our previous report show that QTc interval prolongation occurs sometime between the 9th and 30th days of spaceflight.

Space Flight↗
DOE OSTI2022

Transfer Learning-Based Independent Component Analysis

Understanding the underlying component structure is crucial for multivariate signal analysis. Among all the techniques that try to learn the latent structure, independent component analysis (ICA) is one of the most important and popular methods, which aims to extract independent components from multivariate signals and enables further analysis. For example, in electroencephalogram (EEG) analysis, artifacts filtering and disease detection are conducted based on the independent components of the signals. One critical challenge in existing ICA approaches is that the component extraction accuracy may degrade when the available data of a unit are limited. To address this issue, this paper proposes a transfer learning-based ICA method by innovatively transferring component distribution from a source domain, so that accurate component extraction results can be achieved even when only limited data are available in the target domain. To the best of our knowledge, this is the first work that leverages transfer learning to improve ICA accuracy with limited available data. In particular, we first extract all the independent components from the source domain by maximizing the log-likelihood function with a Newton-like method on a smooth manifold. Then for the target domain, the component with the largest negentropy is extracted in each round. To effectively leverage the knowledge from the source domain and to prevent the negative transfer, we try to find a component in the source domain that matches the component we are extracting. The probability density function of the matched component will then be used to improve the component extraction accuracy if such matched component can be found; otherwise, no knowledge will be transferred. Finally, numerical simulations and a case study with electrocardiogram (ECG) data are conducted, showing the effectiveness of the proposed method in transferring knowledge and reducing negative transfer.

42 ENGINEERING↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Intracardiac Electrical Imaging using the 12-lead ECG: A Machine Learning Approach using Synthetic Data

Current state-of-the-art techniques for non-invasive imaging of cardiac electrical phenomena require voltage recordings from dozens of different torso locations and anatomical models built from expensive medical diagnostic imaging procedures. Here this study aimed to assess if recent machine learning advances could alternatively reconstruct electroanatomical maps at clinically relevant resolutions using only the standard 12-lead electrocardiogram (ECG) as input. To that end, a computational study was conducted to generate a dataset of over 16000 detailed cardiac simulations, which was then used to train neural network (NN) architectures designed to exploit both spatial and temporal correlations in the ECG signal. Analysis over a validation set showed average errors in activation map reconstruction below 1.7 msec over 75 intracardiac locations. Furthermore, phenotypical patterns of activation and the morphology of the activation potential were correctly reconstructed. The approach offers opportunities to stratify patients non-invasively, both retrospectively and prospectively, using metrics otherwise only available through invasive clinical procedures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia

Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.

electrocardiography↗