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On-scalp printing of personalized electroencephalography e-tattoos

Cell Biomaterials 2025年第1期1卷

作者： Luize Scalco de Vasconcelos Yichen Yan Pukar Maharjan Satyam Kumar Minsu Zhang Bowen Yao Hongbian Li Sidi Duan Eric Li Eric Williams Sandhya Tiku Pablo Vidal R. Sergio Solorzano-Vargas Wen Hong Yingjie Du Zixiao Liu Fumiaki Iwane Charles Block Andrew T. Repetski Philip Tan Nanshu Lu Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin TX 78712 USA Department of Materials Science and Engineering University of California Los Angeles Los Angeles CA 90095 USA Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 USA David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Biomedical Engineering The University of Texas at Austin Austin TX 78712 USA Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA

Electroencephalography (EEG) is crucial for diagnosing neurological disorders and facilitating brain-computer interfaces. Traditional EEG setups with wet gels and cumbersome cables are labor intensive, uncomfortable, and degrade over time. Dry, skin-conformable e-tattoos offer a comfortable and user-friendly alternative but struggle with hairy scalps. To tackle this problem, we introduce non-contact digital printing of e-tattoos directly on the hairy scalp. Biocompatible inks are crafted for low-impedance electrodes and high-conductivity interconnects. The fabrication system includes a custom sensor layout design algorithm based on individual head scans and a 5-axis robot controlling a microjet printhead for safe and precise on-scalp ink delivery. After printing, these inks rapidly self-dry into conductive films perfectly conformed to scalp skin, with enhanced wearability, longevity, and skin adhesion compared with gel electrodes and transferred e-tattoos. Motion imagery and error-related potentials are effectively measured by these printed e-tattoos. This innovation heralds a new era in on-body manufacturing of personalized e-tattoos.

关键词： additive manufacture inkjet printing conformable printing on-body manufacture conductive ink flexible electronics stretchable electronics wearable electronics personalized e-tattoo brain-computer interface BCI

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cytoNet: Spatiotemporal network analysis of cell communities (vol 18, e1009846, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1009846页

作者： Mahadevan, A. S. Long, B. L. Hu, C. W. Ryan, D. T. Grandel, N. E. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania United States of America. Department of Bioengineering Rice University Houston Texas United States of America. Systems Synthetic and Physical Biology Program Rice University Houston Texas United States of America. Department of Biomedical Engineering University of Texas at San Antonio San Antonio Texas United States of America. Biophysics Graduate Program University of California Berkeley California United States of America. Department of Oral & Maxillofacial Surgery University of Texas Health Science Center at San Antonio San Antonio Texas United States of America. Department of Electrical and Computer Engineering Rice University Houston Texas United States of America.

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

关键词： Calcium imaging Fluorescence imaging Growth factors Centrality Cell cycle and cell division Endothelial cells Network analysis Stem cells

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Generalizability of splint therapy for temporomandibular joint disorders in the era of precision medicine

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International journal of rheumatic diseases 2023年第1期26卷 170-171页

作者： Shu-Hao Hsu Wei-Wei Liu Yung-Heng Lee Kevin Sheng-Kai Ma School of Medicine Chung Shan Medical University Taichung Taiwan Republic of China. Department of Orthopedics Cishan Hospital Ministry of Health and Welfare Kaohsiung Taiwan Republic of China. Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Republic of China. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts USA. Department of Dermatology Massachusetts General Hospital Boston Massachusetts USA.

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Double-containment coil with enhanced winding mounting for transcranial magnetic stimulation with reduced acoustic noise

arXiv

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arXiv 2020年

作者： Koponen, Lari M. GoetzAngel, Stefan M. Peterchev, V. Department of Psychiatry & Behavioral Sciences Duke University DurhamNC United States Department of Psychiatry & Behavioral Sciences Department of Electrical & Computer Engineering Department of Neurosurgery Duke University DurhamNC United States Department of Psychiatry & Behavioral Sciences Department of Electrical & Computer Engineering Department of Neurosurgery Department of Biomedical Engineering Duke University DurhamNC United States

Objective: This work aims to reduce the acoustic noise level of transcranial magnetic stimulation (TMS) coils. TMS requires high currents (several thousand amperes) to be pulsed through the coil, which generates a loud acoustic impulse whose peak sound pressure level (SPL) can exceed 130 dB(Z). This sound poses a risk to hearing and elicits unwanted neural activation of auditory brain circuits. Methods: We propose a new double-containment coil with enhanced winding mounting (DCC), which utilizes acoustic impedance mismatch to contain and dissipate the impulsive sound within an air-tight outer casing. The coil winding is potted in a rigid block, which is mounted to the outer casing by its acoustic nodes that are subject to minimum vibration during the pulse. The rest of the winding block is isolated from the casing by an air gap, and sound is absorbed by foam within the casing. The casing thickness under the winding center is minimized to maximize the coil electric field output. Results: Compared to commercial figure-of-eight TMS coils, the DCC prototype has 10–33 dB(Z) lower SPL at matched stimulation strength, whilst providing 22% higher maximum stimulation strength than equally focal commercial coils. Conclusion: The DCC design greatly reduces the acoustic noise of TMS while increasing the achievable stimulation strength. Significance: The acoustic noise reduction from our coil design is comparable to that provided by typical hearing protection devices. This coil design approach can enhance hearing safety and reduce auditory co-activations in the brain and other detrimental effects of TMS sound. Copyright © 2020, The Authors. All rights reserved.

关键词： Acoustic noise

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Respiratory event index underestimates severity of sleep apnea compared to apnea-hypopnea index

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SLEEP Advances 2024年第1期5卷 zpad054页

作者： Pitkänen, Minna Nath, Rajdeep Kumar Korkalainen, Henri Nikkonen, Sami Mahamid, Alaa Oksenberg, Arie Duce, Brett Töyräs, Juha Kainulainen, Samu Leppänen, Timo Department of Technical Physics University of Eastern Finland Kuopio Finland Vtt Technical Research Centre of Finland Ltd Kuopio Finland Diagnostic Imaging Center Kuopio University Hospital Kuopio Finland Sleep Disorders Unit Loewenstein Hospital-Rehabilitation Center Raanana Israel Sleep Disorders Centre Department of Respiratory and Sleep Medicine Princess Alexandra Hospital Brisbane Australia Institute for Health and Biomedical Innovation Queensland University of Technology Brisbane Australia Science Service Center Kuopio University Hospital Kuopio Finland School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets (n = 1561). Moreover, TAT-based AHI (AHI_TAT) and TST-based REI (REI_TST) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHI_TAT, and REI_TST were significantly lower than AHI (p < 0.0001, p ≤ 0.002, and p ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHI_TAT, the accuracies were 68.4% and 85.9%, and based on REI_TST, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REI_TST (r = 0.98 and r = 0.99 for the datasets) and least with REI (r = 0.92 and r = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REI_TST the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity. © 2023 The Author(s). Published by Oxford University Press on behalf of Sleep Research Society.

关键词： home testing OSA sleep disordered breathing

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RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-based Semantic Segmentation

arXiv

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arXiv 2021年

作者： He, Shuang Lu, Xia Gu, Jason Tang, Haitong Yu, Qin Liu, Kaiyue Ding, Haozhou Chang, Chunqi Wang, Nizhuan Jiangsu Key Laboratory of Marine Bioresources and Environment Jiangsu Key Laboratory of Marine Biotechnology Co-Innovation Center of Jiangsu Marine Bio-Industry Technology Jiangsu Ocean University Lianyungang222005 China School of Geomatics and Marine Information Jiangsu Ocean University Lianyungang222005 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing211800 China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518055 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China

For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-of-the-art RSI semantic segmentation methods. © 2021, CC BY-NC-ND.

关键词： Semantic Segmentation

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Recent advances in neurotechnologies with broad potential for neuroscience research (vol 23, pg 1522, 2020)

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NATURE NEUROSCIENCE 2021年第4期24卷 611-611页

作者： Vazquez-Guardado, Abraham Yang, Yiyuan Bandodkar, Amay J. Rogers, John A. Center for Bio-Integrated Electronics Northwestern University Evanston IL USA Department of Materials Science and Engineering Northwestern University Evanston IL USA Department of Mechanical Engineering Northwestern University Evanston IL USA Department of Biomedical Engineering Northwestern University Evanston IL USA Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA Querrey Simpson Institute for Bioelectronics Northwestern University Chicago IL USA Department of Neurological Surgery Feinberg School of Medicine Northwestern University Chicago IL USA

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Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis In biomedical Imaging

arXiv

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arXiv 2020年

作者： Wismüller, Axel De, Titas Lochmüller, Eva Eckstein, Felix Nagarajan, Mahesh B. Departments of Biomedical Engineering & Imaging Sciences University of Rochester United States Department of Electrical & Computer Engineering University of Rochester United States Institute of Anatomy and Musculoskeletal Research Paracelsus Medical University Salzburg Salzburg Austria

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p -4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications. Copyright © 2020, The Authors. All rights reserved.

关键词： computerized tomography

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On the Emerging Area of Biocybersecurity and Relevant Considerations

arXiv

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arXiv 2020年

作者： Palmer, Xavier-Lewis Potter, Lucas Karahan, Saltuk Biomedical Engineering Institute Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States Department of Political Science and Geography Old Dominion University NorfolkVA23529 United States

Biocybersecurity is a novel space for the 21st century that meets our innovations in biotechnology and computing head on. Within this space, many considerations are open for and demand consideration as groups endeavor to develop products and policies that adequately ensure asset management and protection. Herein, simplified and brief exploration is given followed by some surface discussion of impacts. These impacts concern the end user, ethical and legal considerations, international proceedings, business, and limitations. It is hoped that this will be helpful in future considerations towards biocybersecurity policy developments and implementations. Copyright © 2020, The Authors. All rights reserved.

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Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms

arXiv

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arXiv 2022年

作者： Ding, Cheng Guo, ZhiCheng Rudin, Cynthia Xiao, Ran Shah, Amit Do, Duc H. Lee, Randall J. Clifford, Gari Nahab, Fadi B. Hu, Xiao Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States School of Nursing Emory University AtlantaGA United States Dept of Epidemiology RSPH Emory University School of Medicine AtlantaGA United States Department of Medicine Emory University School of Medicine AtlantaGA United States School of Medicine University of California at Los Angeles Los AngelesCA United States School of Medicine University of California at San Francisco San FranciscoCA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States Department of Neurology Emory University School of Medicine AtlantaGA United States

Atrial fibrillation (AF) is a common cardiac arrhythmia with serious health consequences if not detected and treated early. Detecting AF using wearable devices with photoplethysmography (PPG) sensors and deep neural networks has demonstrated some success using proprietary algorithms in commercial solutions. However, further advancement of this paradigm of continuous AF detection in ambulatory settings, towards a population-wide screening use case, still faces several challenges, one of which is the lack of large-scale labeled training data. To address this challenge, in this study, we propose to leverage AF alarms from bedside patient monitors to label concurrent PPG signals, resulting in the largest PPG-AF dataset so far (8.5M 30-second records from 24100 patients) and demonstrating a practical approach to build large labeled PPG datasets. Furthermore, we recognize that the AF labels thus obtained contain errors because of false AF alarms generated from imperfect built-in algorithms from bedside monitors. Dealing with label noise with unknown distribution characteristics in this case requires advanced algorithms. We, therefore, introduce and open source a novel loss design, the cluster membership consistency (CMC) loss, to mitigate label errors. By comparing CMC with state-of-the-art methods selected from a noisy label competition, we demonstrate its superiority in multiple aspects including handling label noise in PPG data, resilience to poor-quality signals, and computational efficiency. © 2022, CC BY.

关键词： Large dataset

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