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arXiv 2021年

作者： Ali Vosoughi, M. Wismüller, Axel Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We propose large-scale Extended Granger Causality (lsXGC) and investigate whether it can capture such changes using resting-state fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 126 adult subjects with a childhood diagnosis of ADHD form the Addiction Connectome Preprocessed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s-tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarker for marijuana use. © 2021, CC BY.

关键词： Support vector machines

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Patients with obstructive sleep apnea are at great risk of flavor disorders: a 15-year population-based cohort study

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Clinical Oral Investigations 2023年第1期27卷 183-192页

作者： Huang, Hsin-Hsin Ma, Kevin Sheng-Kai Wu, Min-You Hung, Yao-Min Tsao, Chien-Han Wei, James Cheng-Chung Wen, Wei-Sheng Wang, Yu-Hsun Hung, Shih-Yen Chao, Max Min Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering & Computer Science National Taiwan University Taipei Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine National Yang Ming University Taipei Taiwan Department of Otolaryngology Chung Shan Medical University Hospital Taichung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Division of Allergy Immunology and Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Chi-Mei Medical Center Tainan Taiwan

Objective: Obstructive sleep apnea (OSA) results from upper airway remodeling, which has been suggested to alter sensory and motor neuron function due to hypoxia or snore vibration. This study investigated whether OSA was associated with the risk of flavor disorder (FD). Materials and methods: Seven thousand and eight hundred sixty-five patients with OSA and 7865 propensity score-matched controls without OSA were enrolled between 1999 and 2013 through a nationwide cohort study. The propensity score matching was based on age, sex, comorbidities including hypertension, hyperlipidemia, chronic obstructive pulmonary disease (COPD), asthma, ankylosing spondylitis, and Charlson comorbidity index, and co-medications during the study period, including statins and angiotensin-converting enzyme (ACE) inhibitors. The adjusted hazard ratio (aHR) of incident FD following OSA was derived using a Cox proportional hazard model. A log-rank test was used to evaluate the time-dependent effect of OSA on FD. Age, sex, comorbidities, and co-medications were stratified to identify subgroups susceptible to OSA-associated FD. Results: Patients with OSA were at a significantly great risk of FD (aHR = 1.91, 95% CI = 1.08–3.38), which was time-dependent (log-rank test p = 0.013). Likewise, patients with hyperlipidemia were at a significant great risk of FD (aHR = 2.99, 95% CI = 1.33–6.69). Subgroup analysis revealed that female patients with OSA were at higher risks of FD (aHR = 2.39, 95%CI = 1.05–5.47). Conclusions: Patients with OSA were at significantly great risk of incident FD during the 15-year follow-up period, especially in female patients with OSA. Clinical relevance: Timely interventions for OSA may prevent OSA-associated FD. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Flavor disorder Obstructive sleep apnea Population-based cohort study Smell disorder Taste disorder

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Large-scale augmented granger causality (lsAGC) for connectivity analysis in complex systems: From computer simulations to functional MRI (fMRI)

arXiv

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arXiv 2021年

作者： Wismüller, Axel Vosoughi, M. Ali Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth. As a reference method, we compare our results with cross-correlation, which is typically used as a standard measure of connectivity in the functional MRI (fMRI) literature. Using extensive simulations for a wide range of time-series lengths and two different signal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms cross-correlation at accurately detecting network connections, using Receiver Operator Characteristic Curve (ROC) analysis, across all tested time-series lengths and noise levels. In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange II (ABIDE II) data repository. Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data. © 2021, CC BY.

关键词： Time series

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Acceleration of a CNN-based Heart Sound Segmenter: Implementation on Different Platforms Targeting a Wearable Device

Acceleration of a CNN-based Heart Sound Segmenter: Implement...

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Euromicro Symposium on Digital System Design

作者： Domenico Ragusa Antonio J. Rodriguez-Almeida Stephan Nolting Emanuele Torti Himar Fabelo Ingo Hoyer Alexander Utz Gustavo M. Callico Francesco Leporati Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Institute for Applied Microelectronics (IUMA) University of Las Palmas de Gran Canaria Las Palmas de G.C. Spain Smart Sensor Systemes Fraunhofer IMS Duisburg Germany Fundacion Canaria Instituto de Investigacion Sanitaria de Canarias Las Palmas de G.C. Spain

Cardiovascular diseases (CVDs) are currently one of the leading causes of death worldwide. Being able to detect their symptoms at early stages, even the most hidden ones, is crucial to shorten the diagnosis time and facilitate an early treatment. Currently, the use of continuous tracking systems, mainly based on wearable devices that analyze data using artificial intelligence (AI) algorithms, is being explored to automatically identify, in real time, CVDs symptoms. This could be especially relevant in lowincome countries where there is a shortage of specialized doctors. Therefore, this work focuses on analyzing the real-time execution of the state-of-the-art convolutional neural network (CNN) for heart sound segmentation (HSS) on platforms such as traditional CPU/GPU and the Fraunhofer IMS © AIRISC Core Complex (a RISC-V processor developed for AI). Results revealed that, while all implementations exploiting the CPU/GPU platform proved to be useful in real-time diagnosis from a fixed location, the AIRISC demonstrated its goodness, as a system on a chip (SoC) for a real-time wearable application, when executing a quantized version of the CNN.

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A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury

arXiv

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arXiv 2024年

作者： Dousty, Mehdy Fleet, David J. Zariffa, José Institute of Biomedical Engineering University of Toronto TorontoONM5S 3G9 Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G9 Canada KITE Research Institute University Health Network TorontoONM5G 2A2 Canada Vector Institute TorontoONM5G 1M1 Canada Department of Computer Science University of Toronto TorontoONM5S 2E4 Canada Rehabilitation Sciences Institute University of Toronto TorontoONM5G 1V7 Canada

Hand function is critical for our interactions and quality of life. Spinal cord injuries (SCI) can impair hand function, reducing independence. A comprehensive evaluation of function in home and community settings requires a hand grasp taxonomy for individuals with impaired hand function. Developing such a taxonomy is challenging due to unrepresented grasp types in standard taxonomies, uneven data distribution across injury levels, and limited data. This study aims to automatically identify the dominant distinct hand grasps in egocentric video using semantic clustering. Egocentric video recordings collected in the homes of 19 individual with cervical SCI were used to cluster grasping actions with semantic significance. A deep learning model integrating posture and appearance data was employed to create a personalized hand taxonomy. Quantitative analysis reveals a cluster purity of 67.6% ± 24.2% with with 18.0% ± 21.8% redundancy. Qualitative assessment revealed meaningful clusters in video content. This methodology provides a flexible and effective strategy to analyze hand function in the wild. It offers researchers and clinicians an efficient tool for evaluating hand function, aiding sensitive assessments and tailored intervention plans. © 2024, CC BY-NC-ND.

关键词： Video recording

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L- to U-Band Wavelength Conversion Based on Intermodal Four-Wave Mixing on-Chip

L- to U-Band Wavelength Conversion Based on Intermodal Four-...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Valerio Vitali Kyle R. H. Bottrill Thalía Domínguez Bucio Hao Liu José Manuel Luque González Francisco Jurado-Romero Alejandro Ortega-Moñux Glenn Churchill James C. Gates James A. Hillier Nikolaos Kalfagiannis Daniele Melati Jens H. Schmid Ilaria Cristiani Pavel Cheben J. Gonzalo Wangüemert-Pérez Íñigo Melina-Fernández Frederic Gardes Cosimo Lacava Periklis Petropoulos Optoelectronics Research Centre University of Southampton Southampton United Kingdom Electrical Computer and Biomedical Engineering Department University of Pavia Pavia Italy Telecommunication Research Institute (TELMA) Universidad de Málaga Malaga Spain School of Science and Technology Nottingham Trent University Nottingham United Kingdom Centre de Nanosciences et de Nanotechnologies CNRS Universite Paris-Saclay Palaiseau France Advanced Electronics and Photonics Research Center National Research Council Canada Ottawa ON Canada

We report here a broadband Si-rich silicon nitride wavelength converter based on intermodal four-wave mixing with on-chip mode multiplexing and de-multiplexing functionalities. L- to U-band wavelength conversion of 40... 详细信息

ISBN: (纸本)9798350369311

关键词： Phase shift keying Four-wave mixing Optical mixing Optical wavelength conversion Waveguide lasers Silicon nitride Optical waveguide components Nonlinear optical devices System-on-chip Broadband communication

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Reducing primary cesarean delivery rate through implementation of a smart intrapartum surveillance system

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npj Digital Medicine 2023年第1期6卷 126页

作者： Cheng, Po Jen Cheng, You Hung Shaw, Steven S. W. Jang, Hung Chi Department of Obstetrics and Gynecology Chang Gung Memorial Hospital-Linkou Medical Center Chang Gung University College of Medicine Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Bone and Joint Research Center Department of Orthopaedic Surgery Chang Gung Memorial Hospital-Linkou Taoyuan Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Hongchi Women & Children’s Hospital Taoyuan Taiwan

The rapid changes in clinical maternity situations that occur in a labor and delivery unit can lead to unpredictable maternal and newborn morbidities. Cesarean section (CS) rate is a key indicator of the accessibility and quality of a labor and delivery unit. This retrospective cross-sectional study assesses the nulliparous, term, singleton, vertex (NTSV) cesarean delivery rates before and after the implementation of a smart intrapartum surveillance system. Research data were collected from the electronic medical records of a labor and delivery unit. The primary outcome was the CS rate of the NTSV population. The data of 3648 women admitted for delivery were analyzed. Of the studied deliveries, 1760 and 1888 occurred during the preimplementation and postimplementation periods, respectively. The CS rate for the NTSV population was 31.0% and 23.3% during the preimplementation and postimplementation periods, respectively, indicating a significant 24.7% (p = 0.014) reduction in CS rate after the implementation of the smart intrapartum surveillance system (relative risk, 0.75;95% confidence interval, 0.71–0.80). In the NTSV population, the vaginal and CS birth groups, no significant difference in terms of newborn weight, neonatal Apgar scores, composite neonatal adverse outcome indicator, and the occurrence of the following: neonatal intensive care unit admission, neonatal meconium aspiration, chorioamnionitis, shoulder dystocia, perineal laceration, placental abruption, postpartum hemorrhage, maternal blood transfusion, and hysterectomy before and after the implementation of the smart intrapartum surveillance system. This study reveals that the use of the smart intrapartum surveillance system can effectively reduce the primary CS rate for low-risk NTSV pregnancies without significantly affecting perinatal outcomes. © 2023, The Author(s).

关键词： Population statistics

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Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moiré Superlattice

arXiv

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arXiv 2023年

作者： Lian, Zhen Meng, Yuze Ma, Lei Maity, Indrajit Yan, Li Wu, Qiran Huang, Xiong Chen, Dongxue Chen, Xiaotong Chen, Xinyue Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Lischner, Johannes Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Materials The Thomas Young Centre for Theory and Simulation of Materials Imperial College London United Kingdom Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States Materials Science and Engineering Program School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Strongly enhanced electron-electron interaction in semiconducting moiré superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states1–7. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moiré superlattice, with electrons and holes separated in different layers8,9, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles10,11 with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moiré superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moiré cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moiré superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both12 © 2023, CC BY.

关键词： Excitons

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Intelligent upper-limb exoskeleton integrated with soft wearable bioelectronics and deep-learning for human intention-driven strength augmentation based on sensory feedback

arXiv

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arXiv 2023年

作者： Lee, Jinwoo Kwon, Kangkyu Soltis, Ira Matthews, Jared Lee, Yoon-Jae Kim, Hojoong Romero, Lissette Zavanelli, Nathan Kwon, Youngjin Kwon, Shinjae Lee, Jimin Na, Yewon Lee, Sung Hoon Yu, Ki Jun Shinohara, Minoru Hammond, Frank L. Yeo, Woon-Hong Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul04620 Korea Republic of IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of Medicine AtlantaGA30332 United States Institute for Materials Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an intelligent upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle signals, which are simultaneously computed to determine the user's intended movement. The cloud-based deep-learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 500-550 millisecond response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newton of force while generating displacement of 87 millimeter at maximum. Collectively, the intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted *** Codes 68T40 (Primary) 92C55, 68T99 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Sensory feedback

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MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images

arXiv

引用

arXiv 2023年

作者： Jiang, Hongxu Imran, Muhammad Muralidharan, Preethika Patel, Anjali Pensa, Jake Liang, Muxuan Benidir, Tarik Grajo, Joseph R. Joseph, Jason P. Terry, Russell DiBianco, John Michael Su, Li-Ming Zhou, Yuyin Brisbane, Wayne G. Shao, Wei Department of Electrical and Computer Engineering University of Florida GainesvilleFL32608 United States Department of Medicine University of Florida GainesvilleFL32608 United States Department of Health Outcomes and Biomedical Informatics University of Florida GainesvilleFL32608 United States College of Medicine University of Florida GainesvilleFL32608 United States Department of Bioengineering University of California Los AngelesCA90095 United States Department of Biostatistics University of Florida GainesvilleFL32608 United States Department of Urology University of Florida GainesvilleFL32608 United States Department of Radiology University of Florida GainesvilleFL32608 United States Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Urology University of California Los AngelesCA90095 United States

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://***/mirthAI/MicroSegNet and our dataset is publicly available at https://***/records/10475293. © 2023, CC BY-NC-SA.

关键词： Image segmentation

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