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

作者： Zaylaa, Amira J. Daher, Alaa Ayache, Mohammad Omeirat, Niveen Mahmoud, Razane Hawili, Bader Abou Beirut Arab University Biomedical Engineering Laboratory Biomedical Engineering Program Electrical and Computer Engineering Faculty of Engineering Lebanon

ISBN: (纸本)9798350325850

Osteoporosis is a common disease characterized by low bone density and structural deterioration of bone tissue. For a successful course of treatment and fracture avoidance, early diagnosis of this disease is essential. The aim was to provide a novel method for osteoporosis prediction using Artificial Intelligence (AI)-based framework. The purpose was to predict the likelihood of osteoporosis based on the Bone Mineral Density (BMD), along with other characteristics such as age, weight, height, gender, and Body Mass Index (BMI) extracted from medical reports and images collected from a comprehensive medical center in Lebanon. Three machine-learning algorithms were implemented and tested, Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT). Variety of quantitative statistical metrics were used to evaluate the performance of our framework, upon training and testing our algorithms. The metrics that were employed to evaluate our results included accuracy, precision, sensitivity, and F-score, in addition to the Receiver Operating Characteristic (ROC) Curve and the Area Under the Curve (AUC). Experimental Results demonstrated that both the SVM and LR algorithms achieved the highest accuracy of detection of osteoporosis as compared to existing algorithms applied in this field, with an accuracy of 89%. The sensitivity of diagnosis obtained was 98% by LR and 97% by SVM and surpassed the sensitivity obtained by DT. As such LR showed the best performance. The output of the algorithms could help medical doctors assess patients automatically. These findings demonstrated the potential of AI in osteoporosis prediction and thus prevention, highlighting the significance of early diagnosis. Thereby as a future prospect, choosing carefully the framework is crucial and additional algorithms have to be considered and tested. © 2023 IEEE.

关键词： Decision trees

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A Novel Linguistic Relational Fuzzy C-Means

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IEEE Access 2025年 13卷 89332-89342页

作者： Phaknonkul, Peerawich Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Chiang Mai University Faculty of Engineering Department of Computer Engineering Chiang Mai50200 Thailand Chiang Mai University Biomedical Engineering and Innovation Research Center Biomedical Engineering Institute Chiang Mai50200 Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai50200 Thailand

In real-world applications, sometimes there are uncertainties in the data sets whether from the collection process or from the natural languages. Moreover, the data may come in the form of fuzzy relation. To analyze data, one might use one of popular tools, i.e., a clustering method. There are several types of clustering including clustering on information from objects themselves or clustering on a relational data. Currently, there are few researches on a fuzzy data clustering and a fuzzy relational data clustering. However, those algorithms are either defuzzifying the data or the relation of the data before implementing the clustering Therefore, in this paper, we develop a linguistic relational fuzzy C-means (LRFCM) that works with fuzzy relation of vectors of fuzzy numbers directly. The algorithm is an extension of the regular relational fuzzy C-means (RFCM). The proposed algorithm does not defuzzify fuzzy attributes beforehand. We found that the results from the LRFCM are similar to that from the RFCM. However, the results from the LRFCM can provide information that contains all the uncertainties from the inputs while the RFCM cannot. We also compare the results with the fuzzy C-means clustering. These comparisons show that the LRFCM behaves the same way as the RFCM and the FCM in partitioning the input space into clusters. © 2013 IEEE.

关键词： Fuzzy clustering

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A learning-based approach to fault detection and fault-tolerant control of permanent magnet DC motors

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Journal of engineering and Applied Science 2023年第1期70卷 109页

作者： Sardashti, Abolghasem Nazari, Jamal Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical Biomedical and Mechatronics Engineering Qazvin Branch Islamic Azad University Qazvin Iran

In the context of Industry 4.0, which prioritizes intelligent and efficient solutions for industrial systems, this paper introduces an innovative methodology for fault detection and fault-tolerant control of DC motors. Leveraging the capabilities of machine learning and reinforcement learning, our approach aims to achieve optimal performance while maintaining a low computational burden. At the heart of our strategy lies a reinforcement learning-enhanced proportional-integral controller meticulously designed for precise positioning of DC motors. Through extensive comparative analysis, we establish the superiority of this controller in terms of precision, efficiency, and user accessibility when compared to traditional techniques. To ensure robust fault detection, we synergize a model-based observer with Mahalanobis distance-based outlier analysis, creating a swift and accurate diagnostic method for sensor faults. In cases of sensor malfunctions, an internal model-based control strategy comes into play, enabling the system to uphold its effectiveness despite disruptions. The effectiveness of our proposed methods is vividly demonstrated through simulations in the MATLAB environment, utilizing a DC motor subjected to sensor failures. The results unequivocally highlight the advantages of our approach, showcasing improved precision, faster operation, cost-effectiveness, and streamlined simplicity. As such, our approach finds suitability for industrial applications. In our quest to strike a delicate balance between performance and complexity, our techniques are purposefully crafted to provide intelligent yet pragmatic solutions that promote reliability, safety, and sustainability. This paper contributes to the evolving landscape of intelligent industrial solutions by offering a comprehensive framework that optimizes performance while minimizing complexity and costs. In doing so, we lay the foundation for a more efficient and resilient industrial ecosystem. © 2023, Faculty o

关键词： Fault detection

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Mamba State Space Model (SSM) Method for the Automatic Segmentation of Organs at Risk from Head and Neck CT Images 4

Mamba State Space Model (SSM) Method for the Automatic Segme...

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4th International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Vagenas, Theodoros P. Georgas, Konstantinos Matsopoulos, George K. School of Electrical and Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens Greece

ISBN: (纸本)9798350391183

Radiotherapy (RT) is one of the main treatment modalities using high-energy radiation to eliminate cancer cells in Head and Neck cancer (HNC) for patients worldwide. Advances such as intensity-modulated radiotherapy (IMRT) improve the precision of targeting tumors and prevent damaging healthy tissues. Segmentation of Organs At Risk (OARs) on Computed Tomography (CT) images is crucial for RT treatment planning in order to avoid damaging healthy structures during the sessions. However, manual delineation of OARs is challenging as their size varies and both small and large organs are present in images with low contrast against the background. In this regard, manual delineation cannot be performed in a timely manner and suffers from potential interobserver variability among clinicians, leading to delays in clinical workflow. In this study, we propose an automatic Deep Learning segmentation framework utilizing effectively the recently introduced Mamba State Space Models (SSM) to localize and delineate multiple OARs with high accuracy from the 3D HNC CT images. Due to the Mamba-based encoder, the model can learn the long-range dependencies of the largest organs by efficiently handling large sequences while also segmenting the smaller ones. The proposed model was evaluated on two public datasets, demonstrating superior performance with an average Dice Similarity Coefficient (DSC) of 75.45% and a Hausdorff distance of 8.22 mm against established deep learning models, such as UNet-based and Transformer-based, in segmenting 9 OARs, indicating promising results to support the clinical workflow. © 2024 IEEE.

关键词： computerized tomography

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MapFormer: Attention-based multi-DNN Manager for Throughout & Power Co-Optimization on Embedded Devices 24

MapFormer: Attention-based multi-DNN Manager for Throughout ...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Karatzas, Andreas Anagnostopoulos, Iraklis School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale United States

ISBN: (纸本)9798400710773

In the context of modern services that use multiple Deep Neural Networks (DNNs), managing workloads on embedded devices presents unique challenges. These devices often incorporate diverse architectures, necessitating advanced management solutions to efficiently deploy multi-DNN workloads. Traditionally, the focus has been on improving throughput, while power optimization has received less attention. This paper presents MapFormer, a new manager that uses attention-based mechanisms to enhance both throughput and power efficiency. MapFormer intelligently assigns multi-DNN workloads to different computing components of embedded systems—CPU, GPU, and DLA—and adjusts operational frequencies to optimize power use. Experimental results show that MapFormer significantly improves average throughput under set power budgets by 90.8%, offering a promising approach for managing complex workloads on heterogeneous embedded systems. © 2024 Copyright is held by the owner/author(s).

关键词： Budget control

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Hardware Accelerator for a Power Efficient Single-lead Dry-electrode ECG Wearable Design 46

Hardware Accelerator for a Power Efficient Single-lead Dry-e...

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

作者： Abdou, Abdelrahman Krishnan, Sridhar Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798350371499

Single-lead electrocardiographic (ECG) monitoring wearables are becoming candidate technologies for long-term remote monitoring applications. Current wearable disadvantages include high power consumption from computational complex pre-processing leading to low battery life. A hardware (HW) architecture for dry electrode-based ECG signal processing to increase wearable longevity is proposed. The technology is based off an analog-front end (AFE) chip combined with a field programmable gated arrays (FPGA)-based optimized cubic Hermite interpolation approach for signal processing. This system is deployed on a FPGA board featuring a single-core processor. The architecture uses 0.01 W, utilizes 0.67% and 0.44% of available look-up-tables (LUTs) and flip-flops (FFs) components on FPGA and performed real-time signal processing. Signal quality indexes (SQIs) and signal to noise ratios (SNR) information are computed where the HW processed signals showed an average SNR of 16.4 dB. ECG R-peaks are visually identified, making this architecture suitable for heart rate (HR), and heart rate variability (HRV) estimations in long-term dry-electrode single-lead ECG monitoring applications. © 2024 IEEE.

关键词： Table lookup

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Nanofabricated high turn-density spiral coils for on-chip electromagneto-optical conversion

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Microsystems & Nanoengineering 2024年第2期10卷 181-192页

作者： Ilhan Bok Alireza Ashtiani Yash Gokhale Jack Phillips Tianxiang Zhu Aviad Hai Department of Biomedical Engineering University of Wisconsin–MadisonMadisonWIUSA Department of Electrical and Computer Engineering University of Wisconsin–MadisonMadisonWIUSA Wisconsin Institute for Translational Neuroengineering(WITNe) University of Wisconsin–MadisonMadisonWIUSA

Circuit-integrated electromagnets are fundamental building blocks for on-chip signal transduction,modulation,and tunability,with specific applications in environmental and biomedical micromagnetometry.A primary challenge for improving performance is pushing quality limitations while minimizing size and fabrication complexity and retaining spatial *** efforts have exploited highly involved three-dimensional synthesis,advanced insulation,and exotic material ***,we present a rapid nanofabrication process that employs electron beam dose control for high-turn-density diamond-embedded flat spiral coils;these coils achieve efficient on-chip electromagnetic-to-optical signal *** fabrication process relies on fast 12.3 s direct writing on standard poly(methyl methacrylate)as a basis for the metal lift-off *** with 70 micrometer overall diameters and 49–470 nm interturn spacings with corresponding inductances of 12.3–12.8 nH are *** utilize optical micromagnetometry to demonstrate that magnetic field generation at the center of the structure effectively correlates with finite element modeling *** designs based on our process can be integrated with photolithography to broadly enable optical magnetic sensing and spin-based computation.

关键词： utilize retaining high

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Systematic Comparison of Machine Learning for Activity Recognition in Cross-Subject vs. NonCross-Subject Scenarios: A Preliminary Analysis 8

Systematic Comparison of Machine Learning for Activity Recog...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： Pe, Samuele Nicora, Giovanna Guerra, Bruna Maria Vittoria Sozzi, Stefania Parimbelli, Enea University of Pavia Dept. of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350362138

Human Activity Recognition (HAR) is pivotal for automating the detection and classification of human movements. Technological advances in data collection from sensors and cameras is making automatic HAR increasingly available to support robotics-assisted rehabilitation, where the detection of abnormal patterns in movement and gait can aid clinical evaluation of patients with diverse disorders. HAR typically employs Machine Learning (ML) or Deep Learning (DL) techniques to analyze sensor data patterns for various activities. However, the generalizability of ML/DL models remains underexplored, particularly concerning the impact of different training and evaluation settings. Overfitting is a common challenge, especially in medical applications where user-dependent training data hinder generalization. Investigating the performance of ML and DL algorithms after a cross-subject (CS) and a noncross-subject (NCS) division of the dataset into training and test sets is crucial. This study aims to systematically compare various ML and DL algorithms for HAR, evaluating their performance across the CS and NCS settings to discern the impact of inter-subject variability on model generalizability. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Evaluation of Human Inertial properties in Immersive Simulators for Moth type Boats 6

Real-Time Evaluation of Human Inertial properties in Immersi...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Todesca, Davide Sergenti, Carol Giulietti, Nicola Carnevale, Marco Dept. Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9798350394634

The aim of this work is to present a system designed for real-time markerless evaluation of human inertial properties, to be used in a Human In The Loop (HITL) simulator of a Moth class sailboat. The method is based on human body pose estimation algorithm used for 3D skeleton reconstruction from a multi-camera acquisition. The exploitation of a markerless system enables ease of use compared to marker-based systems, and reduces signal’s disturbances compared to Inertia Measurement Units (IMUs) for body pose reconstruction. The use of multi-camera setup and the integration of YOLOv8-pose model results with image triangulation techniques reduce occlusion occurrence, measurements drift and complexity. The 3D pose obtained is then used to estimate the Center of Mass (CoM) position and the Tensor of Inertia (ToI) of the skipper. The system allows for a real-time computation of the inertial properties of the skipper, thus enabling a real-time update of the mass matrix of the numerical model running the human-in-the loop simulator. This study shows the results achievable by the system in terms of inertia estimation for different skipper’s poses, and compares the cases in which the skipper is modelled as a point mass with real-time position updates with the case in which the full inertia tensor of the skipper is considered. Results show how in contexts like those of Moth class sailboat simulators the possibility to take into account and to vary in real-time the tensor of inertia of the sailor increases the accuracy achievable in the dynamic simulation. © 2018 IEEE.

关键词： Cameras

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 2096-2105页

作者： Zhang, Zhao Lin, Bor-Shyh Peng, Chih-Wei Lin, Bor-Shing Wuyi University College of Mechanical and Electrical Engineering Fujian Wuyishan354300 China National Taipei University College of Electrical Engineering and Computer Science Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde

关键词： Sleep research

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