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computers in Cardiology (CinC)

作者： Giulio Steyde Beniamino Daniele Edoardo Spairani Giovanni Magenes Maria Gabriella Signorini Department of Electronics Information and Bioengineering (DEIB) Politecnico di Milano Milan Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

In pregnancy, diabetes is known to increase the risk of adverse maternal and neonatal outcomes. It would be beneficial to find techniques that allow early investigation of the physio-pathological mechanisms involved to provide clinicians with tools for prevention and therapies. For that, cardiotocography (CTG) is a promising tool. However, the evidence is still scarce and the impact on clinical practice little. In this study, we aim at characterizing the changes induced by gestational diabetes (GDM) on the fetal heart rate series. To do so, we performed a retrospective cohort study on a CTG dataset containing more than 20000 recordings of which 852 belong to 301 GDM-diagnosed patients. We divided the recordings by gestational age (G.A.) into 4 groups (weeks: 31-35, 36, 37,38 to delivery) and for each we identified a control population of equal size matched by comorbidities. We analyzed a comprehensive set of parameters from the time domain, frequency domain and non-linear analysis and assessed variations in median values on each feature. For all G.A. below the 38th week, we found a significant increase in the power in the movement frequency band $(p < 0.01)$ and an increase in the absolute value of Deceleration Reserve $(p < 0.01)$ in $GDMvs$ control. Other significant values were also identified and are discussed in more detail in the paper.

关键词： Pregnancy Pediatrics Fetal heart rate Sociology Medical treatment Recording Diabetes

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Alteration of Cortical Somatosensory Feedback in Post-Stroke Movement Control 46

Alteration of Cortical Somatosensory Feedback in Post-Stroke...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Peng, Rita Huan-Ting Parmar, Nishaal Williamson, Jordan N. Sirpal, Parikshat Mulyana, Beni E. Rajabtabar Darvish, Mahmood Sung, Joohwan Sidorov, Evgeny V. Yang, Yuan Department of Bioengineering Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Clinical Imaging Research Center Stephenson Family Clinical Research Institute Carle Foundation Hospital UrbanaIL United States Department of Electrical and Computer Engineering University of Oklahoma TulsaOK74135 United States Department of Neurology University of Oklahoma Health Sciences Center Oklahoma CityOK73104 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL United States Department of Physical Therapy and Human Movement Sciences Northwestern University ChicagoIL United States

ISBN: (纸本)9798350371499

Sensory feedback is of vital importance in motor control, yet is rarely studied in diseases which frequently result in motor deficiency, such as hemiparetic stroke. This study employs the laterality index (LI) of somatosensory evoked potentials (SEPs) to investigate whether sensory feedback is altered in hemiparetic stroke during movements of the paretic arm, with a hemispheric shift from the lesioned hemisphere toward contralesional hemisphere. Through experimental design involving the isometric lifting of the paretic arms during tactile finger stimulation and the analysis of LI in SEPs P50 and N100, we found: 1) increased contralesional sensory activity in stroke participants when they are receiving sensory input in their paretic hand for both P50 and N100 and 2) the contralesional N100 activity is enhanced when stroke participants are performing an isometric arm lifting task. These results indicate a timedependent shifting of sensory feedback from the sensorimotor areas in the lesioned side to the contralesional side of the stroke brain. © 2024 IEEE.

关键词： Sensory feedback

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A Versatile and Efficient Neuromorphic Platform for Compute-in-Memory with Selector-less Memristive Crossbars

A Versatile and Efficient Neuromorphic Platform for Compute-...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Soumil Jain Gopabandhu Hota Yuhan Shi Sangheon Oh Jiajia Wu Preston Fowler Duygu Kuzum Gert Cauwenberghs Department of Electrical and Computer Engineering UC San Diego La Jolla CA Department of Bioengineering UC San Diego La Jolla CA Institute for Neural Computation UC San Diego La Jolla CA

Memristive crossbar arrays have become essential building blocks in the realization of large-scale neuromorphic systems with high-density synaptic connectivity. Traditionally, memristor-based accelerators are equipped with selector elements to reduce cross-talk through sneak paths along unselected lines. However, due to the large drive strength required for selector elements, it comes at the cost of synaptic crossbar density. Selector- less alternatives require careful design of crossbar peripheral circuits to mitigate or eliminate sneak path-induced cross-talk. We propose a hybrid integrated platform that interfaces a selector- less memristor crossbar array with peripheral row and column instrumentation for array-parallel programming and readout for AI learning and inference applications. The proposed switched-capacitor voltage-sensing instrumentation avoids the need for current-sensing schemes with voltage-clamped sense lines that are typically used to mitigate the sneak path issues in selector- less crossbars but are substantially less energy-efficient than voltage-sensing. Our board-level platform is implemented using commercial off-the-shelf (COTS) data converters and switched capacitors, and is controlled by a Xilinx Spartan-6 FPGA. The system offers programmable sense times to characterize memristors over a wide range of resistances and the capability to switch between a transient-domain measurement and steady-state measurement to offer the desired trade-off between accuracy and energy efficiency during inference parallel readout. We implement a differential weight-encoding scheme to improve the accuracy of matrix-vector multiplication. The system also supports an array-level programming scheme for parallel write access as well as online learning-in-memory for neuromorphic applications through outer-product incremental decomposition of the weight matrix. Thus, our system offers a generic, user-configurable, and versatile platform to support wide dynamic range

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Predicting Response to Neoadjuvant Chemotherapy using Multi-Modal MRI Radiomics and Machine Learning Approaches

Predicting Response to Neoadjuvant Chemotherapy using Multi-...

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IEEE International Symposium on Biomedical Imaging

作者： Abdelrahman Gamal Ahmed Sharafeldeen Eman Alnaghy Reham Alghandour Khadiga M. Ali Sameh Shamaa Amal Aboueleneen Ahmed Elsaid Tolba Samir Elmougy Mohammed Ghazal Sohail Contractor Ayman El-Baz Department of Computer Science Faculty of Computers and Information Mansoura University Egypt Department of Bioengineering University of Louisville USA Department of Radiology Faculty of Medicine Mansoura University Mansoura Egypt Department of oncology Faculty of Medicine Mansoura University Mansoura Egypt Pathology Department Faculty of Medicine Mansoura University Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE Department of Radiology University of Louisville USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

A precise computer-aided diagnosis (CAD) system is introduced to predict two different tumor responses to neoadjuvant chemotherapy (NAC) by studying the correlation between radiological and clinical markers, and treatment response: complete response and stable disease. Predicting the NAC response assists physicians in managing the treatment plan effectively and reduces unnecessary use of NAC. First, the breast tumor is delineated from MRI modalities. Then, the proposed system extracts functional features, including the apparent diffusion coefficient (ADC), from diffusion-weighted imaging (DWI), and 2 nd -order texture features, such as gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM), from various MRI modalities. Afterward, statistical features are extracted from these matrices. Moreover, ADC features are represented statistically using percentiles with 2% intervals. Subsequently, a feature selection approach, namely the genetic algorithm (GA), is employed for each fused features individually (i.e., ADC, GLCM, and GLRLM) to extract the most relevant features. The resulting feature set undergoes a multi-stage feature generation system, introducing new features (i.e., probabilities), which are fused with its input and clinical markers and are then fed into a lightGBM classifier for final prediction. Our system was evaluated on 54 patients using leave-one-subject-out (LOSO) cross-validation, achieving a sensitivity of 89.89%, a specificity of 89.29%, and an accuracy of 89.09%, emphasizing the promise of our system in predicting breast tumor response to NAC.

关键词： Solid modeling Chemotherapy Breast tumors Magnetic resonance imaging Machine learning Feature extraction Breast cancer Genetic algorithms Radiomics Diseases

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Twoviewdensity-Pvt: A Vision Transformer System for Accurate Bi-Rads Classification in Breast Cancer Grading From Mammogram Images

Twoviewdensity-Pvt: A Vision Transformer System for Accurate...

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IEEE International Symposium on Biomedical Imaging

作者： I. Abdelhalim Y. Almalki A. Abbas M. Elsharkawy R. Karam S. Alduraibi M. Ghazal A. Mahmoud S. Contractor A. El-Baz Department of Bioengineering University of Louisville Louisville KY USA Department of Internal Medicine Najran University Najran Saudi Arabia Volunteer at Department of Bioengineering University of Louisville Louisville KY USA Department of Radiology Mansoura University Egypt Department of Radiology Qassim University Buraidah Saudi Arabia Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi UAE Department of Radiology University of Louisville Louisville KY USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

In this paper, a novel vision transformer-based system, TwoViewDensity-PVT, is designed for classifying Breast Cancer (BC) into four BI-RADS categories from mammogram images. It aims to enhance BC grading accuracy and consistency by offering an objective categorization method. The pipeline workflow is inaugurated by identifying Regions of Interest (ROIs) in craniocaudal (CC) and mediolateral oblique (MLO) views, which are then processed by transformers to discern BI-RADS-associated radiomic features. To assist the transformers in differentiating both global and local radiomics, such as mass density, we integrated Improved Receptive Field Block (IRFB) (i.e., multiscale kernel) to streamline this process. TwoViewDensity-PVT derives its name from its dual-view approach and effective identification of mass density relevant to BI-RADS grading. Rigorous testing on 4368 patients across four BI-RADS classes yielded a system sensitivity of 83.89% and specificity of 92.13%.

关键词： Accuracy Sensitivity Machine vision Pipelines Transformers Mammography Breast cancer

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Engineered promoters enable constant gene expression at any copy number in bacteria (vol 36, pg 352, 2018)

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NATURE BIOTECHNOLOGY 2022年第5期40卷 799-799页

作者： Segall-Shapiro, Thomas H. Sontag, Eduardo D. Voigt, Christopher A. Synthetic Biology Center Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Bioengineering and Department of Electrical and Computer Engineering Northeastern University Boston Massachusetts USA

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Investigation of neurovascular coupling in four vigilance states of the human brain by simultaneous EEG-fNIRS measurement and wavelet coherence analysis

Investigation of neurovascular coupling in four vigilance st...

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Optics and the Brain, BRAIN 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Rabbani, Parisa Wang, Xinlong Babawale, Olajide Kim, Jae Gwan Liu, Hanli University of Texas at Arlington Department of Bioengineering ArlingtonTX76010 United States Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of

We conducted wavelet coherence analysis to quantify/image the cerebral neurovascular coupling at infra-slow frequencies during four different awake-to-sleep vigilance states using whole-head, simultaneous EEG and func... 详细信息

ISBN: (纸本)9781557528209

关键词： Infrared devices

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In-Ear EEG Auditory Neurofeedback Towards Unobtrusive Sleep Enhancement

In-Ear EEG Auditory Neurofeedback Towards Unobtrusive Sleep ...

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Custom Integrated Circuits Conference (CICC)

作者： Min Suk Lee Zhaoyi Liu Abhinav Uppal Jiahao Song Akshay Paul Florian Chapotot Esra Tasali Yuchen Xu Gert Cauwenberghs Shu Chien-Gene Lay Department of Bioengineering University of California San Diego CA USA Department of Electrical and Computer Engineering University of California San Diego CA USA Institute for Neural Computation University of California San Diego CA USA Department of Medicine University of Chicago IL USA

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

Sleep is not only essential for maintaining quality of life, but also one of the most important factors in sustaining long-term health and fending off disease. Slow wave sleep (deep sleep) mediates the body's natural mechanisms for replenishing and repair. While pharmacological and other clinical interventions target treatment of sleep disorders, novel approaches are critically needed to improve sleep quality. This review assesses using ear-electroencephalography (ear-EEG) for monitoring and modulating slow wave sleep.

关键词： Application specific integrated circuits Protocols Sleep Reviews Maintenance engineering Electroencephalography Acoustics Neurofeedback Monitoring Diseases

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A Novel Technique of Pulmonary Nodules Auto Segmentation Using Modified Convolutional Neural Networks

A Novel Technique of Pulmonary Nodules Auto Segmentation Usi...

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IEEE International Symposium on Biomedical Imaging

作者： B. Youssef A. Alksas A. Shalaby A. Mahmoud E. van Bogaert S. Contractor M. Ghazal A. Elmaghraby A. El-Baz Department of Bioengineering University of Louisville KY USA Department of Engineering Mathematics and Physics Alexandria University Egypt Lyda Hill Department of Bioinformatics UT Southwestern Medical Center Dallas TX USA Department of Radiology University of Louisville KY USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University UAE Computer Science and Engineering Department University of Louisville KY USA

Lung cancer represents the dominant cause of cancer associated deaths worldwide. Researchers have proposed many methods for medical image analysis of CT scans through the decades but in recent times, deep learning (DL) has gained a lot of consideration in the field of analysis of biomedical images . Image processing techniques are used on image data after extracting CT scan features, to determine if the patient’s found nodule is benign or malignant. The most important of these techniques is segmentation which helps identify the shape,size and volume of the nodule. We experiment with several methods of Convolutional Neural Networks (CNN) to target the best approach for pulmonary nodule segmentation. Our aim is to find the compromise between best accuracy, cheapest processing costs and scalability. First of all, it is important to observe that our method is proposed for the segmentation of lung nodules on a previously defined region of interest (ROI) instead of the whole image as input to our networks to cover the fast processing target and experimenting with 3D U-Net and variations of 3D V-Net. We performed analysis to measures of sensitivity and specificity and compared different segmentation approaches using our proposed modified U-Net with a novel adaptive focal loss function and our implementation of a modified V-Net with different architectures. The modified U-Net with the updated loss gave the best sensitivity and specificity of 0.9132 and 0.9807 respectively.

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Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding

arXiv

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arXiv 2023年

作者： Li, Yuhang Gan, Tianyi Bai, Bijie Işıl, Çağatay Jarrahi, Mona Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los Angeles90095 United States Bioengineering Department University of California Los Angeles90095 United States University of California Los Angeles90095 United States

Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. In this work, we demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network (CNN) based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and the optical-decoder model were experimentally validated using a 3D-printed diffractive network that axially spans Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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