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A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

biomedical engineering Frontiers 2022年第1期3卷 31-47页

作者： Lingyi Zhao Muyinatu A.Lediju Bell Department of Electrical and Computer Engineering Johns Hopkins UniversityBaltimoreUSA Department of Computer Science Johns Hopkins UniversityBaltimoreUSA Department of Biomedical Engineering Johns Hopkins UniversityBaltimoreUSA

The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore,understand,and develop new techniques for diagnosis and *** lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT,multiple studies have demonstrated its promise to diagnose COVID-19 *** the same time,many deep learning models have been built to improve the diagnostic efficiency of medical *** integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients,most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of *** invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition,associated datasets,deep learning models,and comparative performance.

关键词： diagnosis lung Lung

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Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

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Iran Journal of computer Science 2023年第4期6卷 387-396页

作者： Saad, Mohamed N. Zareef, Galena W. Ibrahim, Fatma S. Said, Ashraf M. Hamed, Hisham F. A. Biomedical Engineering Department Faculty of Engineering Minia University Minia Egypt Biomedical Engineering Department Misr University for Science and Technology 6th of October City Egypt Bioinformatics Department Faculty of Computer and Information Minia University Minia Egypt Computer and Software Engineering Department Misr University for Science and Technology 6th of October City Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Electrical and Electronics Engineering Department Faculty of Engineering Minia University Minia Egypt

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Genome-wide association study Genome-wide exploratory analysis Minor allele frequency Single nucleotide polymorphism Variant annotation

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Date Fruit Classification System using Deep Transfer Learning 10

Date Fruit Classification System using Deep Transfer Learnin...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Abuowda, Zainab Ramadan, Shorouk Salam, Nour Gad, Abdalla Yousaf, Jawad Hassan, Taimur Ghazal, Mohammed Almajali, Eqab Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350316353

Nowadays, developing an accurate computer vision system for classifying the type and maturity stage of date fruit is essential for designing an efficient and smart robotic harvesting solution. In this study, we proposed an effective deep transfer learning-based system for automatically classifying five distinct date types on bunches (Naboot saif, Khalas, Barhi, Meneifi, and Sullaj) with four different maturity stages (Immature, Khalal, Rutab, and Tamar) using transfer learning approach on pre-trained deep convolutions neural networks. Resenet-50 and AlexNet networks were trained on the available dataset for the two classification tasks (date types and maturity stage). The findings of performed various experiments suggested that the proposed Resenet-50 outperforms the AlexNet with maximum achieved accuracies of 95.1% and 94.7% for date type and maturity stage classification, respectively. The study's findings are to be utilized in designing an intelligent robotic harvesting mechanism for efficient monitoring and harvesting of date fruits in a natural environment. © 2023 IEEE.

关键词： Harvesting

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Safer Navigation: The AI-Powered Smart Cane for the Visually Impaired 16

Safer Navigation: The AI-Powered Smart Cane for the Visually...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Gad, Abdalla Fteiha, Bara' Alatawi, Jawaher Mohammed, Mahra Almansoori, Mahra Ghazal, Mohammed Yousaf, Jawad Almajali, Eqab Hussain, Abir Jafar Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350381344

This work proposes a flexible, foldable, lightweight, robust, and smart cane to aid visually impaired individuals and patients in navigating unfamiliar environments safely. The developed smart cane is equipped with an ultrasonic sensor, RFID reader, and an AI machine vision sensor for object detection and identification in the range of 30 cm to 9 m. A mobile application with a text-to-voice feature is developed that connects with the cane using Bluetooth to provide voice warnings to the user based on processed sensor output. The developed cane is also equipped with a vibration sensor activated when an obstacle is closer than 30 cm, serving as a backup of voice warnings. Object detection and classification using multiple sources (AI vision sensor, ultrasonic sensor, and RFID readers) ensures robust and uninterrupted operation in case of failure of one sensor. In emergencies, users can send text messages with their location to a pre-selected caretaker's phone number using the installed SOS button. The rechargeable battery offers more than 24 hours of operation and warns users when the battery is low. The prototype has been successfully executed and tested in various environments and aims to improve the safety and quality of life for visually impaired individuals and hospital patients. © 2023 IEEE.

关键词： Object detection

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Infrastructure-dependent ramp-metering control for METANET-s 63

Infrastructure-dependent ramp-metering control for METANET-s

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kamalifar, Ayda Cenedese, Carlo Cucuzzella, Michele Ferrara, Antonella University of Pavia Department of Electrical Computer Science and Biomedical Engineering Pavia27100 Italy Eth Zürich Department of Information Technology and Electrical Engineering Zürich Switzerland Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9798350316339

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies. © 2024 IEEE.

关键词： Traffic congestion

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EEG2Text: Open Vocabulary EEG-to-Text Translation with Multi-View Transformer

EEG2Text: Open Vocabulary EEG-to-Text Translation with Multi...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Liu, Hanwen Hajialigol, Daniel Antony, Benny Han, Aiguo Wang, Xuan Virginia Tech Department of Computer Science BlacksburgVA United States Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA United States Virginia Tech Department of Biomedical Engineering and Mechanics BlacksburgVA United States

ISBN: (纸本)9798350362480

Deciphering the intricacies of the human brain has captivated curiosity for centuries. Recent strides in Brain-computer Interface (BCI) technology, particularly using motor imagery, have restored motor functions such as reaching, grasping, and walking in paralyzed individuals. However, unraveling natural language from brain signals remains a formidable challenge. Electroencephalography (EEG) is a non-invasive technique used to record electrical activity in the brain by placing electrodes on the scalp. Previous studies of EEG-to-text decoding have achieved high accuracy on small closed vocabularies, but still fall short of high accuracy when dealing with large open vocabularies. We propose a novel method, EEG2Text, to improve the accuracy of open vocabulary EEG-to-text decoding. Specifically, EEG2Text leverages EEG pre-training to enhance the learning of semantics from EEG signals and proposes a multi-view transformer to model the EEG signal processing by different t spatial regions of the brain. Experiments show that EEG2Text has superior performance, outperforming the state-of-the-art baseline methods by a large margin of up to 5% in absolute BLEU and ROUGE scores. EEG2Text shows great potential for a high-performance open-vocabulary brain-to-text system to facilitate communication. Our code is available at: https://***/ForeverNightmare/EEG2Text. © 2024 IEEE.

关键词： Electroencephalography

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An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

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computers, Materials & Continua 2022年第12期73卷 6269-6287页

作者： Martin Augustynek Jan Kubicek Jaroslav Thomas Marek Penhaker Dominik Vilimek Michal Strycek Ondrej Sojka Antonino Proto Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of Ostrava17.Listopadu 15Ostrava–Poruba70800Czech Republic

The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is *** paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker’s *** this work,we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming *** electrical model of the heart allows signals generation(right atrium,right ventricle)and the monitoring of the stimulation *** LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in *** results demonstrate the capability of proposed system to evaluate the paced and sensed *** proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing *** addition,the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.

关键词： Artificial heart cardiac conduction system electrical cardiac stimulation pacemaker

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Hand Movements Detection Using EMG Signals for Human-computer Interface and convolution neural network 20

Hand Movements Detection Using EMG Signals for Human-Compute...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nazari, Jamal Nosheri, Azar Ghamangiz Mousavi, Seyed Amirhossein Islamic Azad University Qazvin Branch Department of Electrical Engineering and Mechatronics Engineering Qazvin Iran Islamic Azad University Mashhad Branch Department of Computer Engineering Mashhad Iran Islamic Azad University Mashhad Branch Department of Biomedical Engineering Mashhad Iran

ISBN: (纸本)9798350383942

This article presents a study on the effectiveness of a Convolutional Neural Network -based approach for hand movement detection using electromyography (EMG) signals. The study involved 40 participants and used Myo armbands to record EMG signals. The CNN model consisted of three convolutional layers, three max-pooling layers, and two fully connected layers. The model achieved an F1-score of 0.981, precision of 0.981, and recall of 0.981, outperforming two other machine learning algorithms, SVM and RF. The study's results demonstrate the potential of using CNNs for detecting hand movements using EMG signals, which can be applied in prosthetics and robotics for controlling prosthetic limbs and robotic arms. The study's findings suggest that CNN-based approaches have great potential for future research in the field of EMG-based hand movement detection. © 2024 IEEE.

关键词： Machine learning

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Errors Induced by Partial Pathlength Variations Due to Mammographic Compression in Dynamic Diffuse Optical Breast Imaging 16

Errors Induced by Partial Pathlength Variations Due to Mammo...

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Optical Tomography and Spectroscopy of Tissue XVI 2025

作者： Niknam, Kaiser Paul, Mannu Bardhan Donaldson, Anthony Das, Mini Department of Physics University of Houston HoustonTX United States Department of Biomedical Engineering University of Houston HoustonTX United States Department of Electrical and Computer Engineering University of Houston HoustonTX United States

ISBN: (纸本)9781510683761

Optical imaging methods have the potential to overcome many of the drawbacks posed by current breast imaging modalities. Previous studies have found that mammographic compression induces different hemodynamic effects in cancerous and healthy breast tissue. This effect could be exploited in continuous-wave near-infrared spectroscopic imaging (CW-NIRS) for fast and accurate breast cancer screening. The primary issue with this approach is that breast tissue (and the cancerous mass) is displaced during the compression process, potentially introducing a considerable amount of errors and noise into the NIRS measurements with the current simple models used in estimating blood volume (and/or oxy/deoxy Hb) concentrations. In this work, we examine how these errors change with signal depth with breast compression and investigate methods to correct these based on simulations and experiments. © 2025 SPIE.

关键词： Mammography

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Surrogate Simulation of Subject-Specific Lateral Pinch via Deep Learning 46

Surrogate Simulation of Subject-Specific Lateral Pinch via D...

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

作者： Lindbeck, Erica M. Diaz, Maximillian T. Nichols, Jennifer A. Harley, Joel B. University of Florida Department of Electrical and Computer Engineering GainesvilleFL United States University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering GainesvilleFL United States

ISBN: (纸本)9798350371499

Musculoskeletal modeling and simulation is often a lengthy and computationally expensive process, particularly when developing and using personalized models. We present a deep learning-based adaptive surrogate model for lateral pinch, which accepts both musculoskeletal parameters and muscle activations as input for personalization and simulation. This model matches traditional OpenSim forward dynamics with an average root-mean-squared error (RMSE) of 2.27 N, within standard errors of experimental measurements, while demonstrating sensitivity to both categories of input and performing thousand of simulations in seconds (10-1000x faster than traditional multi-body simulations). In addition to direct use as a surrogate, the differentiable nature of the model may support future use in optimization problems, while its flexibility may support adaptation to modeling of experimental data. © 2024 IEEE.

关键词： Adversarial machine learning

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