Publications
Estimating sedentary breathing rate from chest-worn accelerometry from free-living data
July 20, 2020
Conference Paper
Published in:
42nd Annual Intl. Conf. IEEE Engineering in Medicine and Biology Society, EMBC, 20-24 July 2020.
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Summary
Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was to assess thermal-work strain and avoid heat injuries. The monitor design was thus not optimized to estimate breathing rate. Since breathing rate cannot be accurately estimated during periods of high activity, a qualifier was applied to identify sedentary time periods, totaling 8,867 hours. Breathing rate was estimated for a total of 4,179 hours, or 14% of the total collection and 47% of the sedentary total, primarily during periods of sleep. The breathing rate estimation method was compared to an FDA 510(K)-cleared criterion breathing rate sensor (Zephyr, Annapolis MD, USA) in a controlled laboratory experiment, which showed good agreement between the two techniques. Contributions of this paper are to: 1) provide the first analysis of accelerometry-derived breathing rate on free-living data including periods of high activity as well as sleep, along with a qualifier that effectively identifies sedentary periods appropriate for estimating breathing rate; 2) test breathing rate estimation on a data set with a total duration that is more than 60 times longer than that of the largest previously reported study, 3) test breathing rate estimation on data from a physiological monitor that has not been expressly designed for that purpose.
Summary
Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was to assess thermal-work strain and avoid heat injuries. The monitor design was thus...
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Predicting cognitive load and operational performance in a simulated marksmanship task
July 3, 2020
Conference Paper
Published in:
Front. Human Neurosci., 3 July 2020.
Summary
Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we developed a simulated marksmanship scenario with an embedded working memory task in an immersive virtual reality environment. As participants performed the marksmanship task, they were instructed to remember numbered targets and recall the sequence of those targets at the end of the trial. Low and high cognitive load conditions were defined as the recall of three- and six-digit strings, respectively. Physiological and behavioral signals recorded included speech, heart rate, breathing rate, and body movement. These features were input into a random forest classifier that significantly discriminated between the low- and high-cognitive load conditions (AUC=0.94). Behavioral features of gait were the most informative, followed by features of speech. We also showed the capability to predict performance on the digit recall (AUC = 0.71) and marksmanship (AUC = 0.58) tasks. The experimental framework can be leveraged in future studies to quantify the interaction of other types of stressors and their impact on operational cognitive and physical performance.
Summary
Modern operational environments can place significant demands on a service member's cognitive resources, increasing the risk of errors or mishaps due to overburden. The ability to monitor cognitive burden and associated performance within operational environments is critical to improving mission readiness. As a key step toward a field-ready system, we...
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Integrative sensor networks, informatics, and modeling for precision and preventative medicine
July 1, 2020
Journal Article
Published in:
IEEE J. Biomed. Health, Vol. 24, No. 7, July 2020, pp. 1858-1859.
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Summary
The topics of integrative sensor networks, informatics and modeling bring together the tightly coupled and rapidly developing fields of biomedical and health informatics and body sensor networks. Biomedical and health informatics encompasses methods to extract and communicate information from data in order to impact health, healthcare, life sciences and biomedicine. Body sensor networks provide one means to measure the needed data, through continuous monitoring in both clinical and free-living environments. Developments in these areas were highlighted at two co-located conferences: the 2019 IEEE-EMBS International Conferences on Biomedical and Health Informatics (BHI'19) and Wearable and Implantable Body Sensor Networks (BSN'19). BHI and BSN are long-standing IEEE EMBS conferences that provide a forum for researchers and leaders from academia, government and industry to share technical advances and new initiatives in these important areas. Through an open call for this special issue, eleven papers have been included for publication. The majority were presented in an initial form at the 2018 or 2019 BHI and BSN conferences. Nine of the papers were selected through a rigorous peer review. In addition, two keynote speakers from BHI'19 and BSN'19 have provided short position papers.
Summary
The topics of integrative sensor networks, informatics and modeling bring together the tightly coupled and rapidly developing fields of biomedical and health informatics and body sensor networks. Biomedical and health informatics encompasses methods to extract and communicate information from data in order to impact health, healthcare, life sciences and biomedicine...
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Investigation of the relationship of vocal, eye-tracking, and fMRI ROI time-series measures with preclinical mild traumatic brain injury
October 19, 2019
Abstract
Published in:
Neuroscience 2019
Summary
In this work, we are examining correlations between vocal articulatory features, ocular smooth pursuit measures, and features from the fMRI BOLD response in regions of interest (ROI) time series in a high school athlete population susceptible to repeated head impact within a sports season. Initial results have indicated relationships between vocal features and brain ROIs that may show which components of the neural speech networks effected are effected by preclinical mild traumatic brain injury (mTBI). The data used for this study was collected by Purdue University on 32 high school athletes over the entirety of a sports season (Helfer, et al., 2014), and includes fMRI measurements made pre-season, in-season, and postseason. The athletes are 25 male football players and 7 female soccer players. The Immediate Post-Concussion Assessment and Cognitive Testing suite (ImPACT) was used as a means of assessing cognitive performance (Broglio, Ferrara, Macciocchi, Baumgartner, & Elliott, 2007). The test is made up of six sections, which measure verbal memory, visual memory, visual motor speed, reaction time, impulse control, and a total symptom composite. Using each test, a threshold is set for a change in cognitive performance. The threshold for each test is defined as a decline from baseline that exceeds one standard deviation, where the standard deviation is computed over the change from baseline across all subjects’ test scores. Speech features were extracted from audio recordings of the Grandfather Passage, which provides a standardized and phonetically balanced sample of speech. Oculomotor testing included two experimental conditions. In the smooth pursuit condition, a single target moving circularly, at constant speed. In the saccade condition, a target was jumped between one of three location along the horizontal midline of the screen. In both trial types, subjects visually tracked the targets during the trials, which lasted for one minute. The fMRI features are derived from the bold time-series data from resting state fMRI scans of the subjects. The pre-processing of the resting state fMRI and accompanying structural MRI data (for Atlas registration) was performed with the toolkit CONN (Whitfield-Gabrieli & Nieto-Castanon, 2012). Functional connectivity was generated using cortical and sub-cortical atlas registrations. This investigation will explores correlations between these three modalities and a cognitive performance assessment.
Summary
In this work, we are examining correlations between vocal articulatory features, ocular smooth pursuit measures, and features from the fMRI BOLD response in regions of interest (ROI) time series in a high school athlete population susceptible to repeated head impact within a sports season. Initial results have indicated relationships between...
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Comparison of two-talker attention decoding from EEG with nonlinear neural networks and linear methods
August 8, 2019
Conference Paper
Published in:
Sci. Rep., Vol. 9, No. 1, 8 August 2019, 11538.
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Summary
Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. Here, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the new architecture outperforms the baseline linear stimulus-reconstruction method, improving decoding accuracy from 66% to 81% using wet EEG and from 59% to 87% for dry EEG. Also of note was the finding that the dry EEG system can deliver comparable or even better results than the wet, despite the latter having one third as many EEG channels as the former. The 11-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available for further validation, experimentation, and modification.
Summary
Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which...
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Large-scale Bayesian kinship analysis
September 25, 2018
Conference Paper
Published in:
IEEE High Performance Extreme Computing Conf., HPEC, 25-27 September 2018.
Summary
Kinship prediction in forensics is limited to first degree relatives due to the small number of short tandem repeat loci characterized. The Genetic Chain Rule for Probabilistic Kinship Estimation can leverage large panels of single nucleotide polymorphisms (SNPs) or sets of sequence linked SNPs, called haploblocks, to estimate more distant relationships between individuals. This method uses allele frequencies and Markov Chain Monte Carlo methods to determine kinship probabilities. Allele frequencies are a crucial input to this method. Since these frequencies are estimated from finite populations and many alleles are rare, a Bayesian extension to the algorithm has been developed to determine credible intervals for kinship estimates as a function of the certainty in allele frequency estimates. Generation of sufficiently large samples to accurately estimate credible intervals can take significant computational resources. In this paper, we leverage hundreds of compute cores to generate large numbers of Dirichlet random samples for Bayesian kinship prediction. We show that it is possible to generate 2,097,152 random samples on 32,768 cores at a rate of 29.68 samples per second. The ability to generate extremely large number of samples enables the computation of more statistically significant results from a Bayesian approach to kinship analysis.
Summary
Kinship prediction in forensics is limited to first degree relatives due to the small number of short tandem repeat loci characterized. The Genetic Chain Rule for Probabilistic Kinship Estimation can leverage large panels of single nucleotide polymorphisms (SNPs) or sets of sequence linked SNPs, called haploblocks, to estimate more distant...
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Detecting intracranial hemorrhage with deep learning
July 17, 2018
Conference Paper
Published in:
40th Int. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBC, 17-21 July 2018.
Summary
Initial results are reported on automated detection of intracranial hemorrhage from CT, which would be valuable in a computer-aided diagnosis system to help the radiologist detect subtle hemorrhages. Previous work has taken a classic approach involving multiple steps of alignment, image processing, image corrections, handcrafted feature extraction, and classification. Our current work instead uses a deep convolutional neural network to simultaneously learn features and classification, eliminating the multiple hand-tuned steps. Performance is improved by computing the mean output for rotations of the input image. Postprocessing is additionally applied to the CNN output to significantly improve specificity. The database consists of 134 CT cases (4,300 images), divided into 60, 5, and 69 cases for training, validation, and test. Each case typically includes multiple hemorrhages. Performance on the test set was 81% sensitivity per lesion (34/42 lesions) and 98% specificity per case (45/46 cases). The sensitivity is comparable to previous results (on different datasets), but with a significantly higher specificity. In addition, insights are shared to improve performance as the database is expanded.
Summary
Initial results are reported on automated detection of intracranial hemorrhage from CT, which would be valuable in a computer-aided diagnosis system to help the radiologist detect subtle hemorrhages. Previous work has taken a classic approach involving multiple steps of alignment, image processing, image corrections, handcrafted feature extraction, and classification. Our...
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Machine learning for medical ultrasound: status, methods, and future opportunities
February 28, 2018
Journal Article
Published in:
Abdom. Radiol., 2018, doi: 10.1007/s00261-018-1517-0.
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Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.
Summary
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited...
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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data
November 13, 2017
Journal Article
Published in:
bioRxiv preprint doi: https://doi.org/10.1101/218818, 13 November 2017.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with highresolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.
Summary
Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states...
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A linear algebra approach to fast DNA mixture analysis using GPUs
September 12, 2017
Conference Paper
Published in:
HPEC 2017: IEEE Conf. on High Performance Extreme Computing, 12-14 September 2017.
Summary
Analysis of DNA samples is an important step in forensics, and the speed of analysis can impact investigations. Comparison of DNA sequences is based on the analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5 base pairs. Current forensics approaches use 20 STR loci for analysis. The use of single nucleotide polymorphisms (SNPs) has utility for analysis of complex DNA mixtures. The use of tens of thousands of SNPs loci for analysis poses significant computational challenges because the forensic analysis scales by the product of the loci count and number of DNA samples to be analyzed. In this paper, we discuss the implementation of a DNA sequence comparison algorithm by re-casting the algorithm in terms of linear algebra primitives. By developing an overloaded matrix multiplication approach to DNA comparisons, we can leverage advances in GPU hardware and algorithms for Dense Generalized Matrix-Multiply (DGEMM) to speed up DNA sample comparisons. We show that it is possible to compare 2048 unknown DNA samples with 20 million known samples in under 6 seconds using a NVIDIA K80 GPU.
Summary
Analysis of DNA samples is an important step in forensics, and the speed of analysis can impact investigations. Comparison of DNA sequences is based on the analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5 base pairs. Current forensics approaches use 20 STR loci for analysis...
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