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2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Al Abdi, Rabah M. Zakaria, Amer Al Nashash, Hasan Zalak, Tasnim Goswami, Nandu Jeet, Vishwa Jordan Univ. of Science and Tech. Dept. of Biomedical Engineering Irbid Jordan American University of Sharjah Dept. of Electrical Engineering Sharjah United Arab Emirates American University of Sharjah Master of Science in Biomedical Engineering Program Sharjah United Arab Emirates College of Medicine Mohammed Bin Rashed University of Medicine and Health Sciences Dubai United Arab Emirates

ISBN: (纸本)9781665454742

Peripheral arterial disease (PAD) is the narrowing of the arteries within the lower extremities, which inhibits blood flow to the foot. We present the use of dynamic diffuse optical tomography (DDOT) system to measure blood flow in the foot during venous and arterial occlusions. DDOT is a non-ionizing, contrast-free, and fast imaging modality. The DDOT system consists of 4 sensors, each containing four laser diodes (at wavelengths of 670, 780, 808, and 850 nm) and two silicon photodiodes. We examined 11 healthy subjects with a protocol consisting of five consecutive measurements. Biomarkers were extracted from changes in hemoglobin concentrations during the measurements. The results from a pilot study are presented in this report. The average increase in total hemoglobin was 17.2 ±5.8 μ M during a measurement with 60 mmHg occlusion and 10.4 ± 6.7 μ M during a measurement with 180 mmHg occlusion. The biomarkers can quantify blood perfusion and oxygen consumption in the foot, and they have the potential to detect and monitor PAD. © 2023 IEEE.

关键词： Hemodynamics

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Quantitative Assessment of Gait in Spinocerebellar Ataxia Using Deep Learning

Quantitative Assessment of Gait in Spinocerebellar Ataxia Us...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Ming-Hui Lai Don-Gey Liu Ching-Hwa Cheng Chung-Yu Lai Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Electronic Engineering Feng Chia University Taichung Taiwan Department of Medical Equipment Development and Application Hungkuang University Taichung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Spinocerebellar ataxia is a neurological disorder characterized by impaired neural coordination, leading to unstable gait, coordination difficulties, and other motor abnormalities. This disease significantly impacts patients' daily lives, making it challenging for them to maintain balance, walk smoothly, and even perform fine motor skills. The causes of spinocerebellar ataxia are diverse, including genetic factors, brain injuries, or certain diseases. Many patients with spinocerebellar ataxia require specialized medical care and ongoing monitoring to manage its effects. This study aims to utilize Human Posture Estimation to detect the gait patterns of patients with spinocerebellar ataxia and integrate it with a deep learning model to distinguish between normal and affected individuals. The outcomes offer physicians an objective diagnostic tool and facilitate early detection and intervention for patients with impaired gait, thereby supporting the enhancement or preservation of their quality of life. The current prediction accuracy for the risk of ataxia gait is 80%, with an F1 score of 78.26%. It is necessary to conduct a detailed quantitative assessment by analyzing the gait of patients with spinocerebellar ataxia. Quantitative evaluation of the data allows for an objective assessment of the patient's gait, which helps healthcare professionals to more accurately understand and assess the gait status of patients with spinocerebellar ataxia.

关键词： Deep learning Neurological diseases Accuracy Medical services Motors Feature extraction Skeleton

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Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Predicting Potential Fires in Indonesia by Analyzing VIIRS N...

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International Symposium on Grids and Clouds 2023 in conjunction with HEPiX Spring Workshop, ISGC and HEPiX 2023

作者： Suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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Analyzing Significance of Facial Features for Detection of Parkinson's Disease via Static Images

Analyzing Significance of Facial Features for Detection of P...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Chuang-Chien Chiu Shoou-Jeng Yeh Yen-Chi Sun Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Section of Neurology and Neurophysiology Cheng-Ching General Hospital Taichung Taiwan Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Parkinson's disease (PD) is a neurodegenerative disorder often characterized by motor symptoms such as hypomimia, and a reduction in facial expression. We explored the significance of facial features in identifying PD through static images, focusing on detecting hypomimia. A dataset comprised of PD patients and healthy controls' static facial images were collected and analyzed. Facial feature detection was conducted using OpenCV and Dlib libraries for the precise localization of key landmarks. In the subsequent feature extraction, parameters such as symmetry, eye aspect ratio, mouth corner distance, muscle tension, and mouth aperture ratio were used. Through the standardization of these features, meaningful comparisons across diverse images were made. By analyzing hypomimia-related features alongside traditional facial parameters, the results provide a comprehensive understanding of the role of facial expressions in PD diagnosis. The findings suggested that examining facial features in static images enhances the accuracy of PD detection and understanding of the nuanced motor symptoms associated with the disease.

关键词： Accuracy Parkinson's disease Sociology Mouth Standardization Muscles Motors

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Understanding ADC variation by fat content effect using a dual-function MRI phantom

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European Radiology Experimental 2024年第1期8卷 19页

作者： Liu, Yi-Jui Tsai, Tung-Sheng Li, Ya-Hui Peng, Jo-Hua Chang, Hing-Chiu Peng, Hsu-Hsia Lee, Ying-Chieh Lee, Tung-Yang Liou, Chang-Hsien Lin, Tz-Feng Chew, Fatt-Yang Chou, Ruey-Hwang Juan, Chun-Jung Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital 199 Sec. 1 Xinglong Rd. Hsinchu County Zhubei City 302 Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering Chinese University of Hong Kong Hong Kong Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Institute of Nuclear Engineering and Science National Tsing Hua University Hsinchu Taiwan Department of Fiber and Composite Materials Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan Department of Radiology School of Medicine China Medical University Taichung Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung 406 Taiwan Center for Molecular Medicine China Medical University Hospital Taichung Taiwan Department of Medical Laboratory and Biotechnology Asia University Taichung Taiwan

Background: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. Methods: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the “iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ,” IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. Results: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R² = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. Conclusions: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. Relevance statement: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. Key points: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values

关键词： Adipose tissue Diffusion magnetic resonance imaging Phantoms (imaging) Quality assurance (healthcare) Quality control

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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A Computationally Efficient Algorithm for Estimating Respiratory Rate from Seismocardiogram

SSRN

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SSRN 2024年

作者： Ku, Tienhsiung Lin, Yue-Der Department of Anesthesiology Changhua Christian Hospital Changhua50051 Taiwan Artificial Intelligence Development Center Changhua Christian Hospital Changhua50051 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

Respiratory rate (RR) monitoring is crucial in clinical and healthcare settings. With the advancements in microelectromechanical system (MEMS) technology, monitoring respiration using seismocardiogram (SCG) is emerging as a promising approach. This study presents a computationally efficient algorithm for estimating RR from SCG signal. The proposed algorithm consists of two primary steps: firstly applying Gaussian averaging filter for pre-processing and next computing the averaged spectrum from the scalogram based on complex Morlet wavelet within the specified frequency range. RR is then estimated from the dominant frequency appeared in the averaged spectrum. This study also establishes the analytical relationship between the parameters of Gaussian averaging filter and the sampling frequency of SCG signal, which serves as the design criterion for pre-processing filter. The proposed algorithm undergoes testing on two SCG datasets and has demonstrated its feasibility for estimating RR from SCG signal. Furthermore, the algorithm is also tested on both finger and wrist PPG datasets for RR estimation. The test results present high agreement between the RR estimations from SCG (or PPG) signal and those from respiration signal. Based on these testing results, the proposed algorithm shows significant potential in clinical and healthcare applications. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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Foot hemodynamics using optical tomography: A potential biomarker of peripheral arterial disease

Foot hemodynamics using optical tomography: A potential biom...

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Advances in Science and engineering Technology International Conferences (ASET)

作者： Rabah M. Al Abdi Amer Zakaria Hasan Al Nashash Tasnim Zalak Nandu Goswami Vishwa Jeet Dept. of Biomedical Engineering Jordan Univ. Of Science and Tech. Irbid Jordan Dept. of Electrical Engineering American University of Sharjah Sharjah UAE Master of Science in Biomedical Engineering Program American University of Sharjah Sharjah UAE College of Medicine Mohammed Bin Rashed University of Medicine and Health Sciences Dubai UAE

Peripheral arterial disease (PAD) is the narrowing of the arteries within the lower extremities, which inhibits blood flow to the foot. We present the use of dynamic diffuse optical tomography (DDOT) system to measure blood flow in the foot during venous and arterial occlusions. DDOT is a non-ionizing, contrast-free, and fast imaging modality. The DDOT system consists of 4 sensors, each containing four laser diodes (at wavelengths of 670, 780, 808, and 850 nm) and two silicon photodiodes. We examined 11 healthy subjects with a protocol consisting of five consecutive measurements. Biomarkers were extracted from changes in hemoglobin concentrations during the measurements. The results from a pilot study are presented in this report. The average increase in total hemoglobin was $\mathbf{17.2\pm 5.8}\ \mu \mathbf{M}$ during a measurement with 60 mmHg occlusion and $\mathbf{10.4\pm 6.7} \ \mu \mathbf{M}$ during a measurement with 180 mmHg occlusion. The biomarkers can quantify blood perfusion and oxygen consumption in the foot, and they have the potential to detect and monitor PAD.

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