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Investigating the use of uni-directional and bi-directional long short-term memory models for automatic sleep stage scoring

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Informatics in Medicine Unlocked 2020年 20卷

作者： Fraiwan, Luay Alkhodari, Mohanad Department of Electrical and Computer Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan

In this paper, a study is conducted to investigate the use of a Long Short-Term Memory (LSTM) learning system in automatic sleep stage scoring. The developed algorithm will automatically learn to classify sleep stages from any acquired sleep signals data-set. This allows to resolve the difficulties that are facing experts in manual sleep stage scoring. A total of 39 Polysomnogram (PSG) recordings acquired from the online PhysioNet Sleep-EDF database are used in this study. The PSG recordings are chosen to be from the EEG Fpz-Cz signals only. The database comes with annotation files that include expert manual stage scoring based on the Rechtschaffen & Kales (R&K) scoring manual. The obtained signals go initially through a pre-processing procedure where sleep stages signals are extracted, normalized, and filtered. The resulting sleep signals are trained using a k-fold cross-validation scheme of 10-folds. Prior to the training and classification process, the LSTM network architecture is built using Uni- and Bi-directional structures to utilize both the forward and backward chains of data sequences. At the end, the developed algorithm performance is evaluated and a complete performance summary table is provided relative to other State-of-the-Art deep learning studies. The performance of this study is evaluated initially without the merging of S3 and S4 sleep stages following the R&K manual, which is considered challenging due to the minor differences between the signals. Then, the performance is evaluated following the recent American Academy of Sleep Medicine (AASM) scoring manual with the merging of the two stages as N3. The developed algorithm achieved higher results using the Bi-directional LSTM. In addition, it achieved the highest accuracy among all other studies in the field with 97.28%. Furthermore, Cohen's kappa and F1-score were more than 72% on average between all sleep stages. According to the confusion matrix, the algorithm successfully classified sleep sig

关键词： Classification Deep learning Long short-term memory Recurrent neural network Sleep stage scoring Training

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Uncertainty Driven Workflow Scheduling Using Unreliable Cloud Resources

Uncertainty Driven Workflow Scheduling Using Unreliable Clou...

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IEEE International Symposium on Network Computing and Applications

作者： Panagiotis Oikonomou Kostas Kolomvatsos Nikos Tziritas Georgios Theodoropoulos Thanasis Loukopoulos Georgios Stamoulis Computer Science and Engineering Southern Univ. of Science and Technology Shenzhen China Computer Science and Telecomm. University of Thessaly Lamia Greece Comp. Science and Biomedical Informatics University of Thessaly Lamia Greece Electrical and Computer Engineering University of Thessaly Volos Greece

ISBN: (数字)9781728183268

ISBN: (纸本)9781728183275

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you -go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.

关键词： Task analysis Cloud computing Reliability Uncertainty Decision making Computational modeling Quality of service

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Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging

arXiv

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arXiv 2021年

作者： Cao, Xiaozhi Liao, Congyu Iyer, Siddharth Srinivasan Wang, Zhixing Zhou, Zihan Dai, Erpeng Liberman, Gilad Dong, Zijing Gong, Ting He, Hongjian Zhong, Jianhui Bilgic, Berkin Setsompop, Kawin Department of Radiology Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Biomedical Engineering University of Virginia CharlottesvilleVA United States Center for Brain Imaging Science and Technology College of Biomedical Engineering and Instrumental Science Zhejiang University Zhejiang Hangzhou China Department of Imaging Sciences University of Rochester RochesterNY United States Department of Radiology Harvard Medical School CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States

Purpose: To improve image quality and accelerate the acquisition of 3D MRF. Methods: Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low rank (LLR) constraint and a modified spiral-projection spatiotemporal encoding scheme termed tiny-golden-angle-shuffling (TGAS) were implemented for rapid whole-brain high-resolution quantitative mapping. The LLR regularization parameter and the number of subspace bases were tuned using retrospective in-vivo data and simulated examinations, respectively. B0inhomogeneity correction using multi-frequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect. Results: The proposed MRF acquisition and reconstruction framework can produce provide high quality 1-mm isotropic whole-brain quantitative maps in a total acquisition time of 1 minute 55 seconds, with higher-quality results than ones obtained from the previous approach in 6 minutes. The comparison of quantitative results indicates that neither the subspace reconstruction nor the TGAS trajectory induce bias for T1 and T2 mapping. High quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 minutes using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. Conclusion: The proposed TGAS-SPI-MRF with optimized spiral-projection trajectory and subspace reconstruction can enable high-resolution quantitative mapping with faster acquisition speed. © 2021, CC BY.

关键词： Mapping

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Microwave Frequency Fiber Interferometry in Submarine Deployed Telecommunication Cables

arXiv

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arXiv 2025年

作者： Bogris, A. Simos, C. Simos, I. Wang, Y. Fichtner, A. Deligiannidis, S. Melis, N.S. Mesaritakis, C. Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo12243 Greece Dept. of Physics University of Thessaly Lamia35100 Greece Department of Electrical and Electronics Engineering University of West Attica Egaleo12244 Greece Department of Earth and Planetary Sciences ETH Zurich Zurich Switzerland National Observatory of Athens Institute of Geodynamics Athens Greece Department of Biomedical Engineering University of West Attica Aghiou Spiridonos Egaleo12243 Greece

We operated a microwave frequency fiber interferometer in a telecommunication cable in the Ionian Sea, Greece, for two months. The capability of detecting undersea micro earthquakes (magnitude~1.5), tides and ocean wa... 详细信息

关键词： Interferometry

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Investigating the Underlying Dynamical Structure of Supersonic Flows Using Effective Model Reduction

Investigating the Underlying Dynamical Structure of Superson...

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American Control Conference

作者： Dan Wilson Samir Sahyoun Phil Kreth Seddik M. Djouadi Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Uppsala University Uppsala Sweden Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee Space Institute Tullahoma TN USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

The temporal features of a cylinder-generated shock-wave/transitional boundary-layer interaction (XSWBLI) in response to supersonic flow (Mach 2) are investigated using various model reduction techniques. Experimental data are obtained using schlieren imaging at 100 kHz and image analysis is performed using proper orthogonal decomposition (POD). The POD framework is used as a starting point to define a reduced set of data-driven isostable coordinates that characterize the transient behavior of an underlying dynamical system. Observed unsteady behaviors in the shock-wave/boundary-layer interactions are well-represented as an externally forced dynamical system with a pair of complex-conjugate isostable coordinates. Results are validated against well-established reduction methodologies including POD and spectral POD. These results indicate that the isostable reduced coordinate framework can be used to provide an accurate, low-dimensional representation of the dynamical features of supersonic fluid flow, even when the relationships between underlying dynamical model and observed output are not explicitly known.

关键词： Reduced order model DYNAMICAL SYSTEMS Pods Supersonic schlieren Coordinates Transient performance fluid flow

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Event cause analysis in distribution networks using synchro waveform measurements

arXiv

引用

arXiv 2020年

作者： Niazazari, Iman Livani, Hanif Ghasemkhani, Amir Liu, Yunchuan Yang, Lei Department of Electrical and Biomedical Engineering University of Nevada RenoNV89557 United States Department of Computer Science and Engineering California State University Sanbernardino San BernardinoCA92407 United States Department of Computer Science and Engineering University of Nevada RenoNV89557 United States

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers. Copyright © 2020, The Authors. All rights reserved.

关键词： Waveform analysis

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Medical imaging with deep learning for COVID-19 diagnosis: A comprehensive review

arXiv

引用

arXiv 2021年

作者： Bharati, Subrato Podder, Prajoy Mondal, M. Rubaiyat Hossain Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka1205 Bangladesh Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States

The outbreak of novel coronavirus disease (COVID-19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classification leading to COVID-19 detection. The reviews of recent papers indicate that the highest classification accuracy of 99.80% is obtained when InstaCovNet-19 DL method is applied to an X-ray dataset of 361 COVID-19 patients, 362 pneumonia patients and 365 normal people. Furthermore, it can be seen that the best classification accuracy of 99.054% can be achieved when EDL_COVID DL method is applied to a CT image dataset of 7500 samples where COVID-19 patients, lung tumor patients and normal people are equal in number. Moreover, we illustrate the potential DL techniques in drug or vaccine discovery in combating the coronavirus. Finally, we address a number of problems, concerns and future research directions relevant to DL applications for COVID-19. © 2021, CC BY.

关键词： Coronavirus

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Predicting extreme events from data using deep machine learning: when and where

arXiv

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arXiv 2022年

作者： Jiang, Junjie Huang, Zi-Gang Grebogi, Celso Lai, Ying-Cheng The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi’an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King’s College University of Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University TempeAZ85287 United States

We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex Ginzburg-Landau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world. © 2022, CC BY.

关键词： Forecasting

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Developing personalized models of blood pressure estimation from wearable sensors data using minimally-trained domain adversarial neural networks

arXiv

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arXiv 2020年

作者： Zhang, Lida Hurley, Nathan C. Ibrahim, Bassem Spatz, Erica Krumholz, Harlan M. Jafari, Roozbeh Mortazavi, Bobak J. Department of Computer Science and Engineering Texas A&M University United States Department of Electrical and Computer Engineering Texas A&M University United States Yale School of Medicine Yale University United States Department of Biomedical Engineering Texas A&M University United States

Blood pressure monitoring is an essential component of hypertension management and in the prediction of associated comorbidities. Blood pressure is a dynamic vital sign with frequent changes throughout a given day. Capturing blood pressure remotely and frequently (also known as ambulatory blood pressure monitoring) has traditionally been achieved by measuring blood pressure at discrete intervals using an inflatable cuff. However, there is growing interest in developing a cuffless ambulatory blood pressure monitoring system to measure blood pressure continuously. One such approach is by utilizing bioimpedance sensors to build regression models. A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive. In this paper, we propose the application of the domain-adversarial training neural network (DANN) method on our multitask learning (MTL) blood pressure estimation model, allowing for knowledge transfer between subjects. Our proposed model obtains average root mean square error (RMSE) of 4.80 ± 0.74 mmHg for diastolic blood pressure and 7.34 ± 1.88 mmHg for systolic blood pressure when using three minutes of training data, 4.64 ± 0.60 mmHg and 7.10 ± 1.79 respectively when using four minutes of training data, and 4.48±0.57 mmHg and 6.79±1.70 respectively when using five minutes of training data. DANN improves training with minimal data in comparison to both directly training and to training with a pretrained model from another subject, decreasing RMSE by 0.19 to 0.26 mmHg (diastolic) and by 0.46 to 0.67 mmHg (systolic) in comparison to the best baseline models. We observe that four minutes of training data is the minimum requirement for our framework to exceed ISO standards within this cohort of patients. Copyright © 2020, The Authors. All rights reserved.

关键词： Blood pressure

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From 3-D Light Microscopic Images to Quantitative Insight

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Microscopy and Microanalysis 2020年第S2期5卷 524-525页

作者： B. Roysam A. Can H. Shen K. Al-Kofahi J.N. Turner Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY12180 Biomedical Engineering Rensselaer Polytechnic Institute Troy NY12180 Wadsworth Center New York State Department of Health Albany NY12201

This presentation will describe a common core set of widely applicable image analysis techniques for automated quantitative analysis of volumetric microscope image data. Volumetric (as distinct from stereoscopic) three-dimensional (3-D) Microscopy is a rapidly maturing field offering the ability to image thick (compared to the depth of field) specimens using a variety of instrumentation techniques, and producing arrays of brightness values in three spatial dimensions. Also well developed are methods to correct the acquired images for a variety of physical effects including blur and ***, what is of interest is the best-possible visualization of thick specimens. The next step, increasingly being considered in view of growing computational resources, and progress in image analysis techniques, seeks to quantify many of the processes and effects being studied. In some mainstream fields, such quantitation is essential. For instance, various assays for substance testing in pharmaceutical and chemical industries involve quantitative end points. As an illustration, the Draize assay for ocular irritancy testing of drugs and biochemical products for human use requires counting of live and dead cells that stain differently. Another example is the mouse lymphoma test that requires a 3-D counting of bacterial colonies. Neurobiological assays require morphometry, as well as quantification of changes in neurons as a function of time and various applied stimuli such as drugs, heat, and radiation. Angiogenesis assays require quantification of changes in vascular morphometry. computerized image analysis is a powerful tool for extracting quantitative data from 3-D images for statistical analysis.

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