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34th Annual International Conference of the IEEE engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Ripka, W. L. Ricieri, D. da V. Ulbricht, L. Neves, E. B. Stadnik, A. M. W. Romaneli, E. F. R. Electrical Engineering and Industrial Computing Program Federal University of Technology - Paraná Brazil Physical Education Department Federal University of Paraná Brazil Master Program in Biomedical Engineering Federal University of Technology - Paraná Brazil Federal University of Technology - Paraná. Brazil Physical Education Federal University of Technology - Paraná Brazil Control and Automation Engineering Federal University of Technology - Paraná Brazil

ISBN: (纸本)9781457717871

This study aimed to test a protocol of measurements based on Biophotogrammetry to Analysis of Respiratory Mechanics (BARM) in healthy children. Seventeen normal spirometric children (six male and 11 female) were tested. Their performed maneuvers of forced inspiratory vital capacity were recorded in the supine position. The images were acquired by a digital camera, laterally placed to the trunk. Surface markers allowed that the files, exported to CorelDraw (R) software, were processed by irregular trapezoids paths. Compartments were defined in the thoracic (TX), abdominal (AB) and the chest wall (CW). They were defined at the end of an inspiration and expiration, both maximum, controlled by a digital spirometer. The result showed that the measured areas at the inspiratory and expiratory periods were statistically different (p<0.05). It reflects the mobility of CW and compartments. In conclusion, the proposed method can identify the breathing pattern of the measured subject using images in two dimensions (2D).

关键词： respiratory mechanics chest wall respiratory movements photogrammetry children

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3D navigation for transsphenoidal surgical robotics system based on CT — Images and basic geometric approach

3D navigation for transsphenoidal surgical robotics system b...

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IEEE International Conference on Robotics and Biomimetics

作者： Nonthachai Suratriyanont Jackrit Suthakorn Master Program at the Department of Biomedical Engineering Mahidol University Nakompathom Thailand Department of Biomedical Engineering and the Center for Biomedical and Robotics Technology Mahidol University Nakornpathom Thailand

ISBN: (纸本)9781479927456

This paper presents the use of computed tomography image (CT) to generate a 3D workspace in the area of skull base. Our ultimate goal is to develop a surgical robotic system to aid in the endoscopic transsphenoidal surgery. The 3D workspace can benefit both aspects of robot design and intra operative navigation system. In our system, the 3D model of a skull base area is created by the 3DSlicer program and is exported as Stereo Lithography (STL) format which is composed of faces and vertices for the basic structure. For the intraoperative navigation system, the collision detection of tools and anatomical structures can be done by a geometrics approach.

关键词： Surgery Bones Robots Three-dimensional displays Solid modeling Mathematical model Computed tomography

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Design and implementation of 12 Lead ECG signals interpretation system

Design and implementation of 12 Lead ECG signals interpretat...

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International Conference on Instrumentation, Communications, Information Technology, and biomedical engineering (ICICI-BME)

作者： Richard Mengko Fredrick Sutjiady Biomedical Engineering Institute of Technology Bandung Indonesia Electrical Engineering Master Program Institute of Technology Bandung Indonesia

This paper presents the development of an ECG interpretation software tool that is based on the procedures that is normally done by a medical expert. A set of algorithms is developed here to duplicate the steps that are normally followed manually by a medical expert when analyzing an ECG print out. The software will detect irregularities in the ECG graph and marked it, as a tool to help the medical expert. The experiment is done using the PTB Diagnostic ECG Database (***), and the test is done by comparing its results with the diagnostic notes included in the header of each ECG database file.

关键词： Electrocardiography Software Heart Medical diagnostic imaging Rhythm biomedical engineering Electrodes

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FDG-PET as a pre-operative biomarker for predicting and optimizing response to subcallosal cingulate area deep brain stimulation

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Biological Psychiatry 2025年

作者： Gavin J.B. Elias Sarah A. Iskin Michelle E. Beyn Uyiosa Omere Sakina J. Rizvi Amanda K. Ceniti Alexandre Boutet Daphne Voineskos Sidney H. Kennedy Andres M. Lozano Jürgen Germann Joint Department of Medical Imaging University of Toronto Toronto Canada Division of Neurosurgery Department of Surgery University of Toronto & University Hospital Network Toronto Canada Department of Psychiatry University Health Network & University of Toronto Toronto Canada Master of Arts Program in the Social Sciences (MAPSS) University of Chicago Chicago USA Krembil Brain Institute Toronto Canada Center for Advancing Neurotechnological Innovation to Application (CRANIA) Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada

Background Deep brain stimulation targeting the subcallosal cingulate area (SCC-DBS) has emerged as a promising therapy for treatment-resistant depression (TRD). However, only half to two thirds of patients experience meaningful clinical response, highlighting the need for biomarkers that could help to optimize SCC-DBS outcomes. Our group previously showed that a support vector machine (SVM) incorporating pre-operative FDG-PET glucose metabolism values from frontal pole, anterior cingulate cortex, and temporal pole could retrospectively classify treatment response in 21 TRD patients with 81.0% accuracy. Here, we assessed the out-of-sample performance and wider applicability of this putative biomarker. Methods Baseline FDG-PET data were preprocessed and fed into an SVM classifier. This model, which employed the three aforementioned regional inputs, was trained and tuned using the familiar 21-patient cohort and tested on an unseen TRD validation set (n=35). Within the combined cohort, we also explored glucose metabolism’s potential influence on previously demonstrated relationships between white matter tract stimulation and clinical outcome. Results Our model classified out-of-sample response status with 77.1% accuracy (80.0% precision; 87.0% recall; 0.83 F1 score).This performance proved statistically significant in permutation testing (p permute =0.008) and exceeded that of an alternative, clinically informed SVM. In addition, we found that patients with lower temporal pole metabolism showed stronger coupling between uncinate fasciculus engagement (approximated using electrode localization and activation modelling) and clinical outcome (p=0.027). Conclusions These results corroborate the validity of FDG-PET models as a tool for predicting SCC-DBS outcomes and underscore their value in refining patient selection and further personalizing DBS treatment.

关键词： depression deep brain stimulation subcallosal cingulate positron emission tomography machine learning prediction ACC anterior cingulate cortex DBS deep brain stimulation FDG fluorodeoxyglucose HAMD-17 Hamilton rating scale for depression LOOCV leave-one-out cross-validation MNI Montreal Neurological Institute rCMglc regional cerebral glucose metabolism SCC subcallosal cingulate area SVM support-vector machine TFCE threshold-free cluster-enhancement TRD treatment-resistant depression VTA volume of tissue activated

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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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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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The comparison study between distinct physical fitness norms and their ECG signals under graded exercise intensities and recovery

The comparison study between distinct physical fitness norms...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Lin, Shyan-Lung Lin, Tung-Yi Huang, Cheng-Yi Hsu, Yu-Liang Chang, Hsing-Cheng Department of Automatic Control Engineering Feng Chia University 100 Wenhwa Rd. Seatween Taichung Taiwan Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University 100 Wenhwa Rd. Seatween Taichung Taiwan Office of Physical Education and Sports Affairs Feng Chia University 100 Wenhwa Rd. Seatween Taichung Taiwan

ISBN: (纸本)9781509048977

In this paper, we perform the comparison study of ECG signals for subjects at distinct ends of cardiopulmonary fitness. The cardiopulmonary signals were acquired under rest condition, three graded exercise intensities, and recovery periods. The participants were recruited from university freshmen with cardiopulmonary fitness norm in top, middle, and bottom groups. Spectral analysis was performed to study the hear rate variability (HRV) from recorded ECG signals. From the view of biomedical signal and statistical analysis, results of current study are expected to provide valuable scientific basis for further evaluation of physical fitness assessment and exercise reinforcement. © 2017 IEEE.

关键词： Electrocardiograms

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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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Overcoming the Blood-Brain Tumor Barrier with Docetaxel-Loaded Mesoporous Silica Nanoparticles for Treatment of Temozolomide-Resistant Glioblastoma

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ACS Applied Materials and Interfaces 2024年第17期16卷 21722-21735页

作者： Hsu, Tsung-I Chen, Yi-Ping Zhang, Rong-Lin Chen, Zih-An Wu, Cheng-Hsun Chang, Wen-Chang Mou, Chung-Yuan Chan, Hardy Wai-Hong Wu, Si-Han Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University and National Health Research Institutes Taipei110 Taiwan International Master Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei110 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei110 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei110 Taiwan Nano Targeting & Therapy Biopharma Inc. Taipei110 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei110 Taiwan Department of Chemistry National Taiwan University Taipei106 Taiwan

While temozolomide (TMZ) has been a cornerstone in the treatment of newly diagnosed glioblastoma (GBM), a significant challenge has been the emergence of resistance to TMZ, which compromises its clinical benefits. Additionally, the nonspecificity of TMZ can lead to detrimental side effects. Although TMZ is capable of penetrating the blood-brain barrier (BBB), our research addresses the need for targeted therapy to circumvent resistance mechanisms and reduce off-target effects. This study introduces the use of PEGylated mesoporous silica nanoparticles (MSN) with octyl group modifications (C8-MSN) as a nanocarrier system for the delivery of docetaxel (DTX), providing a novel approach for treating TMZ-resistant GBM. Our findings reveal that C8-MSN is biocompatible in vitro, and DTX@C8-MSN shows no hemolytic activity at therapeutic concentrations, maintaining efficacy against GBM cells. Crucially, in vivo imaging demonstrates preferential accumulation of C8-MSN within the tumor region, suggesting enhanced permeability across the blood-brain tumor barrier (BBTB). When administered to orthotopic glioma mouse models, DTX@C8-MSN notably prolongs survival by over 50%, significantly reduces tumor volume, and decreases side effects compared to free DTX, indicating a targeted and effective approach to treatment. The apoptotic pathways activated by DTX@C8-MSN, evidenced by the increased levels of cleaved caspase-3 and PARP, point to a potent therapeutic mechanism. Collectively, the results advocate DTX@C8-MSN as a promising candidate for targeted therapy in TMZ-resistant GBM, optimizing drug delivery and bioavailability to overcome current therapeutic limitations. © 2024 The Authors. Published by American Chemical Society.

关键词： Tumors

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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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ECG heart-beat classification using multimodal image fusion

arXiv

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arXiv 2021年

作者： Ahmad, Zeeshan Tabassum, Anika Guan, Ling Khan, Naimul Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoON Canada Master of Data Science program Ryerson University TorontoON Canada

In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart-beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end-to-end deep learning. We perform experiments on PhysioNet's MIT-BIH dataset for five different arrhythmias in accordance with the AAMI EC57 standard and on PTB diagnostics dataset for myocardial infarction (MI) classification. We achieved an state-of-an-art results in terms of prediction accuracy, precision and recall. © 2021, CC BY.

关键词： Extraction

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