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9th International Conference on Applied System Innovation, ICASI 2023

作者： Usman, Muhammad Lin, Chun-Ling Ming Chi University of Technology International Ph.D Program in Innovative Technology of Biomedical Engineering and Medical Devices New Taipei City Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei City Taiwan

ISBN: (纸本)9798350398380

Fifteen novices completed a Zentangle task while their brain activity was measured with electroencephalography (EEG) and their concentration emotional state, stress, and anxiety levels were evaluated with questionnaires. The study found increased activity in theta, alpha, beta, and gamma ranges associated with relaxation, focus, and mental activity. Participants reported improved concentration, emotional state, and reduced stress and anxiety levels after completing the task, suggesting the potential of Zentangle to enhance cognitive and emotional well-being. © 2023 IEEE.

关键词： Electroencephalography

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A Scattered Wireless EEG Recording System

A Scattered Wireless EEG Recording System

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IEEE biomedical Circuits and Systems (BIOCAS)

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

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.

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An Iterative Algorithm for Accurate Estimation of Power System Forced Oscillation Parameters

An Iterative Algorithm for Accurate Estimation of Power Syst...

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IEEE General Meeting Power& Energy Society

作者： Luke Dosiek Sanjay Hosur Department of Electrical Computer and Biomedical Engineering Union College Schenectady NY

This paper proposes an iterative method of estimating power system forced oscillation (FO) amplitude, frequency, phase, and start/stop times from measured data. It combines three algorithms with favorable asymptotic statistical properties: a periodogram-based iterative frequency estimator, a Discrete-Time Fourier Transform (DTFT)-based method of estimating amplitude and phase, and a changepoint detection (CPD) method for estimating the FO start and stop samples. Each of these have been shown in the literature to be approximate maximum likelihood estimators (MLE), meaning that for large enough sample size or signal-to-noise ratio (SNR), they can be unbiased and reach the Cramer-Rao Lower Bound in variance. The proposed method is shown through Monte Carlo simulations of a low-order model of the Western Electricity Coordinating Council (WECC) power system to achieve statistical efficiency for low SNR values. The proposed method is validated with data measured from the January 11, 2019 US Eastern Interconnection (EI) FO event. It is shown to accurately extract the FO parameters and remove electromechanical mode meter bias, even with a time-varying FO amplitude

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Unsupervised Segmentation of Pulmonary Regions in 3D CT Scans Optimized Using Transformer Model 27th

Unsupervised Segmentation of Pulmonary Regions in 3D CT Sc...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Sharafeldeen, Ahmed Khelifi, Adel Ghazal, Mohammed Yaghi, Maha Mahmoud, Ali Contractor, Sohail El-Baz, Ayman Department of Bioengineering University of Louisville LouisvilleKY United States Computer Science and Information Technology Department Abu Dhabi University Abu Dhabi United Arab Emirates Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Radiology University of Louisville LouisvilleKY United States

ISBN: (纸本)9783031783463

This paper presents a new automated unsupervised segmentation system to accurately delineate the pulmonary region in 3D computed tomography (CT) scans. It operates on a multi-dimensional joint probability model, which leverages a deep learning-based transformer model to optimize the log-likelihood of that probabilistic model. This probability model integrates appearance probability models, that represent various radiodensity distributions in both lung and chest areas within the 3D CT volume using linear combination of Gaussian (LCG), along with their spatial probability model, generated based on a 3D Markov Gibbs random field (MGRF), with potentials estimated analytically. Finally, the generated segmentation is further refined using a transformer model to maximize the log-likelihood of a given region. The proposed method’s efficacy is assessed by analyzing 3D chest scans of 28 patients diagnosed with varying severities of COVID-19. Four metrics are employed for evaluation, namely, Dice similarity coefficient (DSC), overlap coefficient, Hausdorff distance (HD), and absolute volume difference (AVD). The proposed system demonstrates outstanding performance, with scores of 95.60±1.37%, 91.61±2.51%, 6.56±2.68, and 6.23±3.52, respectively. These findings underscore the potential of the proposed system in delineating both normal and pathological lung regions in CT images, when compared to its individual components as well as six state-of-the-art (SOTA) segmentation methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Application of CohereNet to Photoacoustic Data for Non-Invasive, In Vivo, Subcutaneous Imaging

Application of CohereNet to Photoacoustic Data for Non-Invas...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Timaná, José Fernandes, Guilherme S. P. Pavan, Theo Z. Bell, Muyinatu A. Lediju Pontificia Universidad Católica Del Perú Department of Engineering Peru University of São Paulo Ffclrp Department of Physics Brazil Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Johns Hopkins University Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350346459

Short-lag spatial coherence (SLSC) beamforming of photoacoustic signals reduces acoustic clutter and enhances the contrast of underlying signals of interest. However, the original SLSC imaging algorithm is also known to be computationally expensive to implement. CohereNet, a custom deep neural network (DNN), was previously introduced to address the computational complexity of SLSC beamforming when applied to ultrasound data. Although it is conceivable that CohereNet can be translated to photoacoustic data, this translation is challenged by differences in data patterns and imaging depths when compared to ultrasound data, and it requires a similar diversity of photoacoustic training data to that present in ultrasound training data. Therefore, we propose to translate CohereNet to photoacoustic imaging after taking advantage of the diversity of signal-to-noise ratios (SNRs) available with transcutaneous in vivo data containing various levels of acoustic clutter. Raw channel data were acquired from the distal forearm of 18 volunteers with a variety of skin constitutive pigmentation, with 12 volunteers designated for training, 3 for validation, and the remaining 3 for testing. Time-delayed kernels and corresponding spatial coherence functions were computed and utilized as DNN training inputs and outputs, respectively. The correlation between standard central processing unit (CPU) SLSC and DNN SLSC images in the test set were 0.76, 0.78, and 0.84 when acquired with 750 nm, 810 nm, and 870 nm wavelengths, respectively. In addition, a mean SNR improvement of 2.5 dB was achieved with DNN SLSC imaging (relative to CPU SLSC imaging), due to the smoother coherence functions created with CohereNet. Results highlight the versatility of CohereNet, particularly when translating from ultrasound to photoacoustic data after appropriate steps are taken to achieve success, with no evidence of overfitting to training data. © 2023 IEEE.

关键词： Deep neural networks

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A 1D Residual CNN with SMOTE-Tomek and Cosine Annealing for Enhanced ECG Arrhythmia Classification

A 1D Residual CNN with SMOTE-Tomek and Cosine Annealing for ...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Feng Jiang Yang Wei Lim Siti Anom Ahmad Faisul Arif Ahmad Luthffi Idzhar Ismail Ahmed Faeq Hussein Fakhrul Zaman Rokhani Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Department of Electrical and Electronics Engineering Faculty of Engineering Universiti Putra Malaysia Department of Biomedical Engineering Faculty of Engineering Al-Nahrain University Baghdad Iraq

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper proposes a 1D residual convolutional neural network (CNN) for classifying arrhythmias based on electrocardiogram (ECG) signals. The additional residual blocks and skip connections effectively alleviate the gradient problem in deep neural networks by providing a direct path for gradient propagation and enhancing the feature information propagation. In addition, the SMOTE-Tomek technique was demonstrated in this work to mitigate the effects of class imbalance caused by a large amount of data during model training and effectively increase the SNR of the training set. The robustness of the model is increased by adding the cosine annealing technique and L 2 regularization. The proposed model was evaluated with various performance metrics, resulting in an accuracy of $\mathbf{9 8. 7 4 \%}$ , sensitivity of $\mathbf{9 8. 3 5 \%}$ , specificity of $\mathbf{9 1. 8 6 \%}$ , precision of $\mathbf{9 7. 9 5 \%}$ , and F1 score of $\mathbf{9 8. 1 4 \%}$ on the MIT-BIH Arrhythmia Database. The result shows better performance for classifying previously unseen data for the proposed approach as compared to the state-of-the-art works.

关键词： Training Annealing Sensitivity Arrhythmia Electrocardiography Robustness Convolutional neural networks Signal to noise ratio Residual neural networks Overfitting

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Detecting overlapping communities in social networks using a modified segmentation by weighted aggregation approach 3

Detecting overlapping communities in social networks using a...

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3rd International Conference on Data Science, E-Learning and Information Systems, DATA 2021

作者： Kashef, Rasha Electrical Computer and Biomedical Engineering Department Ryerson University Canada

ISBN: (纸本)9781450388382

Detecting communities of common behaviors, interests, and interactions in social networks is essential to model a network's structure. Overlapping community detection is an NP-Hard problem. Several solutions have been proposed;however, most of these techniques are computationally expensive. We have developed a fast-hierarchical algorithm using the notion of segmentation by weighted aggregation. Experimental results on synthetic and real benchmark networks show that the proposed algorithm effectively finds communities (Clusters) with varied overlap and non-exhaustiveness structures. Our method outperforms the state-of-the-art hierarchical clustering algorithms measured by the F-measure and the computational time. © 2021 Association for Computing Machinery.

关键词： Coarsening

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Fractional Brownian motion with fluctuating diffusivities

arXiv

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arXiv 2024年

作者： Pacheco-Pozo, Adrian Krapf, Diego Department of Electrical and Computer Engineering School of Biomedical Engineering Colorado State University Fort CollinsCO80523 United States

Despite the success of fractional Brownian motion (fBm) in modeling systems that exhibit anomalous diffusion due to temporal correlations, recent experimental and theoretical studies highlight the necessity for a more comprehensive approach of a generalization that incorporates heterogeneities in either the tracers or the environment. This work presents a modification of Levy's representation of fBm for the case in which the generalized diffusion coefficient is a stochastic process. We derive analytical expressions for the autocovariance function and both ensemble- and time-averaged mean squared displacements. Further, we validate the efficacy of the developed framework in two-state systems, comparing analytical asymptotic expressions with numerical simulations. © 2024, CC BY.

关键词： Brownian movement

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Insights on diagnostic signals in single-phase and three-phase induction motors in single-phasing fault

Insights on diagnostic signals in single-phase and three-pha...

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IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Marcello Minervini Lucia Frosini Alberto Meloni Riccardo De Tullio Lorenzo Mantione Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

The aim of this paper is twofold. On the one hand, the applicability of the well-known diagnostic techniques, based on the motor current and stray flux signature analysis, has been experimentally tested both on small single-phase induction motors and on large on-field three-phase induction motors. On the other hand, the single phasing fault in three-phase induction motors, which has rarely been treated in the literature, has been experimentally analyzed and compared to the behavior of the single-phase motor, in order to find characteristic signatures of this fault. Time and frequency domain analyses were performed on current, axial and radial stray flux, and torque signals. The experimental study highlighted the following insights: i) classical motor current signature analysis cannot be easily applied to permanent-capacitor single-phase machines; ii) stray flux analysis reveals the presence of additional harmonic sidebands, even in healthy three-phase induction motors; iii) a rough analysis of the third harmonic in the current spectrum may be sufficient to diagnose the single phasing fault.

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Towards the Development of a SWIR-LEDs Based Optoelectronic System for Urea Monitoring During Haemodialytic Therapy

Towards the Development of a SWIR-LEDs Based Optoelectronic ...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Elisabetta Bodo Valentina Bello Sabina Merlo Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9798350345995

Chronic kidney disease (CKD) has been one of the most severe public health issues in recent decades. Patients affected by CKD require a complicated and expensive treatment such as hemodialysis. The management of hemodialysis patients have been a particular challenge for nephrologists during the COVID-19 pandemic [1]. The accepted measure of dialysis dose is based on the removal of urea from the blood pool. Urea concentration is usually evaluated with the aid of blood samples that are analyzed in a clinical laboratory, but there is still the need of continuous monitoring of the dialysis efficiency to optimize clinical treatments. Hence, we have investigated and demonstrated an optofluidic sensing platform to detect urea content in water solutions by comparing light transmittance across a flat microfluidic channel with and without fluid. We considered the effect of absorption in the wavelength bands around $\lambda=1.45\mu \mathrm{m}$ and $\lambda=2.15\mu \mathrm{m}$ , where water and urea exhibit characteristic peaks of absorbance [2]. In the instrumental configuration (Fig. 1(a)), radiation provided by two LEDs crosses the microfluidic device and is finally detected in time domain with an amplified InGaAs photodiode, connected to the oscilloscope for data visualization and acquisition. The microfluidic device is a rectangular section borosilicate glass capillary (Vitrocom, NJ, USA) with two extremities provided with heat shrink tubes to facilitate injection of the fluid into the channel. Nominal channel length and depth of the channel are 50 mm and 1 mm, respectively.

关键词： Borosilicate glass Indium gallium arsenide LED sources Optical systems Photodiodes Transmittance

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