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2023 IEEE biomedical Circuits and Systems Conference, BioCAS 2023

作者： Pashaei, Vida Huan, Junjun Chen, Haoyang Osman, Mohamed S. E. A. Kothapalli, Sri-Rajasekhar Majerus, Steve J. A. Mandal, Soumyajit Case Western Reserve University Computer and Systems Engineering Department of Electrical ClevelandOH44106 United States University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States Pennsylvania State University Department of Biomedical Engineering State CollegePA16802 United States

ISBN: (纸本)9798350300260

Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes custom body-conformal probes used within an image-guided platform for chronic and patient-specific FUS neuromodulation. The probes contain separate ultrasound imaging and modulation arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dualmode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam towards the target. © 2023 IEEE.

关键词： body-conformal transducer arrays image-guided therapy Ultrasound imaging

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Reduced Number of Parameters for Predicting Post-Stroke Activities of Daily Living Using Machine Learning Algorithms on Initiating Rehabilitation

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Informatica (Slovenia) 2021年第4期45卷 571-581页

作者： Alqudah, Ali Mohammad Al-Hashem, Munder Alqudah, Amin Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan Department of Computer Engineering Yarmouk University Irbid Jordan

The estimation of the Barthel Index scale (BI) is a significant method for measuring the performance of Activities Daily Living (ADL), where the prediction of ADL is crucial for providing rehabilitation care management and recovery for patients after stroke, therefore in this paper, nine various Machine Learning (ML) algorithms were implemented in a medical dataset contains 776 records from 313 patients 208 of them are men: 208 and 150 are women with multiple features collected from them for predicting and classifying the BI status as clinical decision support for determining the ADL of post-stroke patients. Meanwhile, we have applied feature selection using the chi-squared test to reduce the number of features in the dataset. The results showed that the Decision Tree (DT), XGBoost (XGB), and AdaBoost (ADB) classifiers performed the highest performance achieved with 100% correctness in terms of accuracy, sensitivity, specificity, error, and Area Under Curve (AUC) on both the full and reduced features datasets. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Patient rehabilitation

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Functional Arm Movement Classification in Stroke Survivors Using Deep Learning with Accelerometry Data

Functional Arm Movement Classification in Stroke Survivors U...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Tran, Tan Chang, Lin-Ching Lum, Peter S. The Catholic University of America School of Engineering Department of Electrical Engineering and Computer Science WashingtonDC United States The Catholic University of America School of Engineering Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350324457

The rehabilitation of the upper extremity (UE) plays a pivotal role in the recovery process for stroke survivors. Despite the routine practice of UE rehabilitation, assessing its effectiveness poses a significant challenge. Evaluation methods vary, ranging from clinical assessments of UE motor skills and patient feedback to the utilization of wearable technology equipped with accelerometer or gyro sensors. In this study, we employed deep learning approaches such as Multi-Layer Perceptron, Long Short-term Memory, and Gated Recurrent Units, in conjunction with data from accelerometry sensors embedded in wrist-wearable Inertial Measurement Units. Our findings demonstrate the successful differentiation between functional and non-functional UE movements in individuals recovering from strokes with 90% and 74% of classification accuracy for intra-subject and inter-subject models, respectively. The methodologies employed in this study exhibit consistency and reliability, providing precise outcomes for individual and acceptable outcomes for cross-participant evaluations. Notably, our advanced models directly utilized raw accelerometry data instead of manual extracted features that was often used in traditional machine learning algorithms. These innovative techniques offer cost-effective and adaptable tools for monitoring UE functionality in real-world settings. The insights derived from this study hold the potential to be transformative in tailoring rehabilitation strategies for adults affected by strokes. © 2023 IEEE.

关键词： deep learning rehabilitation stroke upper extremity

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Representations of fricatives in subcortical model responses: Comparisons with human consonant perceptiona)

Representations of fricatives in subcortical model responses...

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作者： Hamza, Yasmeen Farhadi, Afagh Schwarz, Douglas M. McDonough, Joyce M. Carney, Laurel H. Department of Biomedical Engineering University of Rochester RochesterNY14627 United States Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Depts. of Neuroscience and Biomedical Engineering University of Rochester RochesterNY14627 United States Department of Linguistics University of Rochester RochesterNY14627 United States Depts. of Biomedical Engineering Neuroscience and Electrical and Computer Engineering University of Rochester RochesterNY14627 United States

Fricatives are obstruent sound contrasts made by airflow constrictions in the vocal tract that produce turbulence across the constriction or at a site downstream from the constriction. Fricatives exhibit significant intra/intersubject and contextual variability. Yet, fricatives are perceived with high accuracy. The current study investigated modeled neural responses to fricatives in the auditory nerve (AN) and inferior colliculus (IC) with the hypothesis that response profiles across populations of neurons provide robust correlates to consonant perception. Stimuli were 270 intervocalic fricatives (10 speakers × 9 fricatives × 3 utterances). Computational model response profiles had characteristic frequencies that were log-spaced from 125 Hz to 8 or 20 kHz to explore the impact of high-frequency responses. Confusion matrices generated by k-nearest-neighbor subspace classifiers were based on the profiles of average rates across characteristic frequencies as feature vectors. Model confusion matrices were compared with published behavioral data. The modeled AN and IC neural responses provided better predictions of behavioral accuracy than the stimulus spectra, and IC showed better accuracy than AN. Behavioral fricative accuracy was explained by modeled neural response profiles, whereas confusions were only partially explained. Extended frequencies improved accuracy based on the model IC, corroborating the importance of extended high frequencies in speech perception. © 2023 Acoustical Society of America.

关键词： Frequency response

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Hybrid-RIS Empowered UAV-Aided ISAC Systems 100

Hybrid-RIS Empowered UAV-Aided ISAC Systems

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Saikia, Prajwalita Jee, Anand Singh, Keshav Tsiftsis, Theodoros A. Boulogeorgos, Alexandros-Apostolos A. National Sun Yat-sen University Institute of Communications Engineering Kaohsiung804 Taiwan Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi110016 India University of Thessaly Department of Informatics & Telecommunications Lamia35100 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece

ISBN: (纸本)9798331517786

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Review of Imbalanced Datasets and Resampling Techniques in Network Intrusion Detection System 8

A Review of Imbalanced Datasets and Resampling Techniques in...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Rajasa, Mahesa Cadi Rahma, Fayruz Rachmadi, Reza Fuad Pratomo, Baskoro Adi Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Islam Indonesia Department of Informatics Yogyakarta Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350344691

The rapid development of network connections and the widespread use of Internet of Things (IoT) devices has increased network traffic. The surge in network traffic has created new vulnerabilities in cyberspace, making it vulnerable to cyber-attacks. To address this challenge, researchers have turned to intelligent techniques, especially machine learning and deep learning, to improve the detection of network traffic attacks. However, a common problem arises: the data imbalance problem, where normal samples occur more often than attack samples, which hurts the performance and classification of machine learning or deep learning models. This study conducted a systematic literature review to identify the imbalanced datasets and the use of resampling techniques for addressing data imbalances in network intrusion detection research. We found four widely-used imbalanced datasets: NSL-KDD, CIC-IDS2017, UNSW-NB15, and KDD-Cup 1999. Researchers used three resampling approaches to tackle the imbalance problem: oversampling, undersampling, and hybrid sampling (combining oversampling and undersampling approaches). Researchers and practitioners can improve the security and efficiency of attack detection across network traffic by applying resampling techniques. © 2023 IEEE.

关键词： Internet of things

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Blood Leak Detection System in Hemodialysis Due to Venous Needle Dislodgement

Blood Leak Detection System in Hemodialysis Due to Venous Ne...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Syarifah, Zahrotus Sardjono, Tri Arief Fatoni, Muhammad Hilman Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Biomedical Engineering Surabaya Indonesia

ISBN: (纸本)9781665476508

Venous Needle Dislodgement (VND) is one of the life-threatening complications that might occur during hemodialysis. In this research, a device that can identify blood leakage caused by VND has been developed, allowing it to activate the blood pump and produce audible and visual alerts. The sensor is made up of four Light Dependent Resistor (LDR) sensors that are arranged in an array to look for the presence of blood. The sensor can identify a drop of 0.05 ml blood within 0.5-1 second. The accuracy of the sensor is 99.92%, 100%, 99.96%, and 99.89%. The Hetero Associate Memory (HAM) approach is used to determine the level of risk displaying 100% and 100% sensitivity in the results of specificity testing. When blood is discovered by sensors with three levels of risks, the system has the ability to activate auditory and visual alerts and stop the blood pump. © 2022 IEEE.

关键词： Needles

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Interferometric dynamic laser speckle imaging

Interferometric dynamic laser speckle imaging

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Optics and the Brain, BRAIN 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Zilpelwar, Sharvari Cheng, Xiaojun Boas, David A. Department of Electrical and Computer Engineering United States Department of Neurophotonics Center Boston University MA02215 United States Department of Biomedical Engineering Boston University MA02215 United States

We developed interferometric dynamic laser speckle imaging (iDLSI) capable of three-dimensional volumetric measurements of the blood flow. Here, we present the numerical and analytical model for g2,iDLSI(τ) and perfo... 详细信息

ISBN: (纸本)9781957171210

关键词： Full field optical coherence tomography Laser imaging Laser speckle Photons Systems design Three dimensional measurement

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Post-Stroke Lesion Segmentation using 2D and 3D Dense U-net 7

Post-Stroke Lesion Segmentation using 2D and 3D Dense U-net

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7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Mansour, Rena Khreiss, Zeinab Sbeity, Fatima Ali, Mohamad Abou Kassem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon Lebanese University Department of Physics & Electronics Lebanon University of the Basque Country Department of Computer Science & Artificial Intelligence Spain Notre Dame University Department of Electrical &computer Engineering Lebanon

ISBN: (纸本)9798350325850

Strokes are brain attacks that cause death. Survivors suffer from damage to their brain (known as lesions). Detecting and segmenting these lesions using imaging techniques is crucial for medical diagnosis and treatment. However, manual post-stroke lesion segmentation is challenging and not yet fully automated. This research aims to assess the effectiveness of the Dimensional Dense U-Net architecture in this task. The architecture has shown promising results in other segmentation tasks, and we aim to evaluate its performance on the Anatomical Tracing of Lesion after Stroke (ATLAS) dataset. Initially, we employed a 3D version of the model and later switched to a 2D version when the 3D results were unsatisfactory. Among numerous trials, the 3D dense architecture achieved its highest dice score of 13% on patients with a single lesion in the left hemisphere, using 110 images. The 2D dense architecture achieved the highest dice score among all using 1000 random images. Each set was split into 70% for training, 20% for validation and 10% for testing. The training dice score was 87%, with a validation dice score of 9.8%. © 2023 IEEE.

关键词： Architecture

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Circumstance Evaluation Using Extreme Value Analysis on Charging Station Data: The Case of DEI Blue in Greece 14th

Circumstance Evaluation Using Extreme Value Analysis on Cha...

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14th International Conference on Information, Intelligence, Systems, and Applications, IISA 2023

作者： Panagoulias, Dimitrios P. Sarmas, Elissaios Marinakis, Vangelis Tsihrintzis, George A. Department of Informatics University of Piraeus Piraeus18534 Greece Decision Support Systems Lab School of Electrical and Computer Engineering National Technical University of Athens Zografou-Athens15773 Greece

ISBN: (纸本)9783031674259

We apply Extreme Value Analysis on sets of Electric Vehicle (EV) charging data with the goal to extract indications in the data of future trends in the demand for EV charging. The datasets have been provided by DEI blue, a service designed to provide integrated e-mobility solutions to individuals, companies and public administration throughout Greece. Eventhough the data are limited as they have been collected during a period of only twelve months, our analysis still reveals trends in the data highlighting a probable seasonality. The seasonality is observed during periods of extreme heat, regional population mobility and incoming visitors. These occurrences impact technical aspects of battery efficiency, grid capacity and a shift of behavioural usage patterns. Our findings cannot be ignored by policy makers as they are expected to intensify in the years to come due to the fast-rising number of EVs in use. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electrification

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