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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Islam, Md Tarikul Benavides, Juliana Prakash, Ravi McGuire, Laura Charbel, Fady Siegel, Amanda P. Erricolo, Danilo Lin, James Gelovani, Juri Avanaki, Kamran The Richard and Loan Hill Department of Biomedical Engineering University of Illinois Chicago United States Department of Neurosurgery University of Illinois Chicago United States Department of Electrical and Computer Engineering University of Illinois Chicago United States Department of Biomedical Engineering Wayne State University United States

ISBN: (纸本)9781510683860

Preterm neonates are highly susceptible to intraventricular hemorrhage (IVH) due to the vulnerability of their developing periventricular blood vessels, which remain vulnerable until around 36 weeks of gestation. Transfontanelle ultrasound imaging (TFUI) is the standard diagnostic tool for neonatal brain injuries;however, its accuracy is limited in detecting early-stage hemorrhages due to poor sensitivity and specificity. Thermoacoustic imaging (TAI), which combines the high contrast of electromagnetic absorption with the high resolution of ultrasound imaging, presents a promising alternative to address these limitations. In this study, we developed a novel linear-array TAI system designed for detecting neonatal brain hemorrhages through the fontanelle. The system utilizes a commercially available linear-array ultrasound transducer and employs short microwave pulses to induce acoustic signals based on the tissue's absorption properties. The system was characterized by penetration depth study in biological phantoms (chicken breast). Then, the system was evaluated using ex-vivo sheep brain hemorrhage models (sheep brain) with varying degrees of artificially induced hemorrhages. Our results showed that the system successfully detected hemorrhages as small as 0.1 mL, whereas conventional ultrasound was limited to detecting hemorrhages of 0.4 mL or larger. These findings demonstrate the potential of TAI as a clinically viable imaging modality for neonatal brain hemorrhage detection. © 2025 SPIE.

关键词： Ultrasonography

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Harnessing dielectric metasurfaces for light-matter interactions in the infrared region 15

Harnessing dielectric metasurfaces for light-matter interact...

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Photonic and Phononic Properties of Engineered Nanostructures XV 2025

作者： Biswas, Shovasis Kumar Adi, Wihan Beisenova, Aidana Rosas, Samir Arvelo, Eduardo Romero Yesilkoy, Filiz Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States

ISBN: (纸本)9781510685024

Resonant metasurfaces exhibit exceptional subwavelength light-trapping capabilities crucial for advanced biochemical sensors and surface-enhanced spectroscopy. While mid-infrared light-matter interactions have predominantly operated in the weak coupling regime, exploring the strong coupling regime—marked by hybrid polariton states—remains underexplored. Notably, low-loss dielectric metasurfaces with quasi-bound states in the continuum (q-BIC) offer ideal platforms for coherent energy exchange between their high-Q cavities and molecular resonances. This study delves into q-BIC resonances at mid-infrared wavelengths identifying key parameters governing the transition from weak to strong coupling. Moreover, we introduce a novel transitional coupling zone where polaritons are challenging to spectrally distinguish. Additionally, we analyze molecules' transition dipole strength, damping rate, and the total number of molecules coupled to a single cavity, offering insights to optimize dielectric metasurfaces for VSC. These findings provide crucial guidelines for enhancing VSC across diverse molecular transitions, anticipating transformative impacts on polaritonic chemistry. © 2025 SPIE.

关键词： Light sensitive materials

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DENSE-Guided Deep Motion Networks Accounted by Large Rotations to Improve Myocardial Strain Analysis from Routine Cine MRI 22

DENSE-Guided Deep Motion Networks Accounted by Large Rotatio...

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22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Lei, Pengcheng Xing, Jiarui Fang, Faming Epstein, Frederick H. Zhang, Miaomiao University of Virginia Department of Electrical and Computer Engineering United States School of Computer Science and Technology East China Normal University Shanghai China University of Virginia Health System Department of Biomedical Engineering United States

ISBN: (纸本)9798331520526

Myocardial strain imaging provides a valuable tool for detecting subclinical left ventricular (LV) dysfunction and adding prognostic value in assessing various types of heart disease. Recent studies have utilized highly accurate strain-dedicated techniques, such as displacement encoding with stimulated echoes (DENSE), to train a deep learning (DL) framework to predict the myocardial displacements/deformations from routine cine balanced steady state free precession (bSSFP) images. However, these methods have shown limited performance in capturing the large rotational motion of the myocardium associated with twist and torsion over time, which are important aspects of myocardial mechanics. To address this gap, this paper introduces a novel DENSE-guided DL network that explicitly accounts for large rotational motion to further improve strain analysis of standard cine bSSFP images. Specifically, our proposed network includes two key components: (i) a time-series rotation estimation network employing a 3D convolutional encoder-decoder architecture to model the large rotational dynamics of the LV myocardium over time, and (ii) a radial motion prediction network based on deformable image registration. The output of these two sub-networks was integrated and refined through a fusion network to predict the final myocardial displacements, supervised by DENSE ground truth. Experimental results show that our method improves the accuracy of myocardial strain with effectively captured large rotations. © 2025 IEEE.

关键词： Rotation

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Arabic text sentiment analysis and emotion classification using transformers 11

Arabic text sentiment analysis and emotion classification us...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mossad, Nada Mohamed, Youssef Fares, A. Zaky, Ahmed B. Alexandria21934 Egypt Benha University The Computer Systems Engineering Program Faculty of Engineering at Shoubra The Department of Electrical Engineering Egypt

ISBN: (纸本)9798350369328

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately. © 2023 IEEE.

关键词： Sentiment analysis

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NutriA: A Responsive Web App to Monitor Nutrition and Clinical Outcomes in Inflammatory Bowel Disease Patients

NutriA: A Responsive Web App to Monitor Nutrition and Clinic...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Petrini, Giorgia Pala, Daniele Lenti, Marco Vincenzo Lepore, Federica Bosoni, Pietro Quaglini, Silvana Lanzola, Giordano University of Pavia Department of Computer Electrical and Biomedical Engineering Pavia Italy University of Pavia First Department of Internal Medicine Pavia Italy

ISBN: (纸本)9798350386226

Inflammatory Bowel Disease (IBD) is a term that includes two chronic conditions characterized by inflammation of the gastrointestinal tract, i.e., Ulcerative Colitis and Crohn's Disease. IBD can be associated to symptoms that significantly reduce quality of life and can lead to serious complications, therefore proper disease monitoring and treatment is necessary. To this end, Telemedicine has been identified as a useful tool, since it can ease the patients' burden on the management of nutrition and pharmacological treatment, and can improve the connection with the physicians. In this paper, we present NutriA, a new responsive web application for the management of IBD patients in Italy, created to assist patients with diet, medications and symptoms monitoring, optimizing the follow-ups frequency and providing direct assistance by the doctors. Besides being useful to improve the patients' well-being, NutriA is also designed to be used in a new clinical study on the effects of malnutrition on IBD treatment. © 2024 IEEE.

关键词： Telemedicine

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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A Scattered Wireless EEG Recording System

A Scattered Wireless EEG Recording System

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2023 IEEE biomedical Circuits and Systems Conference, BioCAS 2023

作者： Han, Yiming Zhao, Linran Stephany, Raymond G Hsieh, Ju-Chun Wang, Huiliang Jia, Yaoyao The University of Texas at Austin Department of Electrical and Computer Engineering United States The University of Texas at Austin Department of Biomedical Engineering United States

ISBN: (纸本)9798350300260

This paper presents a scattered wirelessly powered Electroencephalographic (EEG) recording system with 32 standalone devices to enable untethered, continuous, long-term EEG recording. Each device incorporates a wireless low-power EEG recording ASIC, a receiver (Rx) coil, and a hydrogel electrode in a compact cube (1.2 cm × 1.2 cm × 0.5 cm). The 32 EEG devices are wirelessly powered by a transmitter (Tx) coil array, under the control of a central control unit, through an inductive link operating at 60 MHz. The 60 MHz carrier also serves as the data link, enabling forward data transmission of user commands from the central control unit to EEG devices and back data transmission from EEG devices to the central control unit. In each EEG device, the ASIC will decode the user commands that are loaded onto the inductive link carrier by a novel synchronized pulse width demodulator at a data rate of 4 Mbps to control the ASIC digitizing the EEG signal or transmitting the EEG data. The EEG analog frontend (AFE) with a gain of 45 dB, bandpass of 0.03 Hz to 800 Hz, and input-referred noise (IRN) of 4.5 μVrms amplifies and filters EEG signals. Then, the 10-bit successive approximation register analog-to-digital converter (SAR-ADC) with an effective number of bits (ENOB) of 9.6 bits digitizes the amplified EEG signals. The backscatter-based data Tx transfers the EEG data to the central control unit via load-shift-keying (LSK) modulation at a data rate of 3.75 Mbps. The overall ASIC and the prototype of the scattered wirelessly powered EEG recording system have been successfully evaluated on human subjects. © 2023 IEEE.

关键词： EEG recording inductive link scattered structure synchronized pulse width modulation

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SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for Identifying AMD Grades

SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： El-Den, Niveen Nasr Elsharkawy, Mohamed Ghazal, Mohammed Mahmoud, Ali Sandhu, Harpal Mahdi, Hani El-Baz, Ayman Department of Computer and System Engineering Ain Shams University Egypt Department of Bioengineering University of Louisville LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368741

Accurate early diagnosis is crucial in addressing Age-related Macular Degeneration (AMD), a chronic retinal disease that is a leading cause of blindness among the elderly. Medical imaging, particularly fundus imaging, is essential in facilitating timely detection and intervention. Due to the variability in image sizes within our dataset, this paper introduces the SABiT-MNet model, which effectively discriminates between healthy retinas, dry AMD, and wet AMD. The model integrates a novel scale-adaptive (SA) approach by combining an autoencoder with Big Transfer (BiT) as its backbone. Unlike traditional resizing methods, which often result in the loss of critical diagnostic information, the SA model dynamically adjusts to varying image sizes, preserving key retinal features essential for accurate diagnosis. The primary aim of this architecture is to retain crucial details in fundus images to ensure precise classification. In this study, 648 subjects were recruited through the Comparisons of AMD Treatments Trials study group, sponsored by the University of Pennsylvania. Experimental results demonstrate that the proposed SABiT-MNet model outperforms state-of-the-art approaches, including transformer-based models, achieving superior diagnostic accuracy. The model recorded performance metrics of 94% accuracy, 97% sensitivity, and 93.94% specificity. To further validate the robustness of the system, we tested it on the public ODiR dataset, where it achieved similarly promising results, confirming the effectiveness of our approach. © 2025 IEEE.

关键词： AMD Deep Learning fundus photography

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Detecting Cardiovascular Disease From PPG Signals using Machine Learning 4

Detecting Cardiovascular Disease From PPG Signals using Mach...

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4th IEEE International Multidisciplinary Conference on engineering Technology, IMCET 2023

作者： Sadek, Tamara Ahmad, Julia Khoury, Fadi Badawe, Heba Khraiche, Massoud American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Biomedical Engineering Department Beirut Lebanon

ISBN: (纸本)9798350313826

Cardiovascular diseases (CVD) are the leading cause of death worldwide. Early detection of CVD allows for the use of effective and inexpensive interventions. CVD prediction using machine learning has achieved high accuracy using multiple data types such as Photoplethysmography (PPG), Electrocardiogram (ECG), and Blood Pressure (BP). However, these biological signals cannot all be reliably collected in real-time from a single wearable device and necessitate patients to be equipped with several devices. In this work, we trained a machine learning model to predict CVD using only PPG data collected from wearable devices, achieving an accuracy of 88% along with an F1-score of 90%. The information that this system can provide the patients with regarding their health status would enable the early detection and management of CVD, eventually decreasing the risk of morbidity. © 2023 IEEE.

关键词： Photoplethysmography

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Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview 17

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Aliyev, Ilkin Svoboda, Kama Adegbija, Tosiron Fellous, Jean-Marc University of Arizona Department of Electrical & Computer Engineering TucsonAZ United States University of Arizona Departments of Psychology and Biomedical Engineering TucsonAZ United States

ISBN: (纸本)9798331530471

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuro-morphic systems that fully exploit the computational advantages of sparse SNNs. © 2024 IEEE.

关键词： Neural network models

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