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Optical Tomography and Spectroscopy of Tissue XV 2023

作者： Ioussoufovitch, Seva Diop, Mamadou School of Biomedical Engineering Collaborative Training Program in Musculoskeletal Health Research Bone & Joint Institute Western University London Canada Dept. of Medical Biophysics Western University London Canada Imaging Program Lawson Health Research Institute London Canada

ISBN: (纸本)9781510658578

Early treatment improves rheumatoid arthritis (RA) prognosis;however, 30% of patients still fail their first treatment and current methods can take as long as 3–6 months to detect treatment failure. Time-domain near-infrared diffuse optical imaging (TD-DOI) has the potential to track gradual changes in RA disease activity and could be a more sensitive technique for RA treatment monitoring. Nevertheless, there has been very little investigation into the relationships between TD-DOI and the specific physiological changes that occur in RA. To this end, this work’s objective was to investigate the effects of RA-associated physiological changes on TD-DOI images in silico. Virtual finger phantoms—derived from an MRI segmentation of a healthy human finger—were used to simulate changes in 5 physiological parameters that are typically affected in RA. Four levels of disease activity were considered for each parameter and each parameter was altered individually;parameter alterations were then translated into changes in phantom geometry and optical properties. MCXLAB was used to simulate the propagation of time-domain light (800 nm) through phantoms’ proximal interphalangeal joints and Poisson noise was added to the TD-DOI data to simulate experimental conditions. Spatiotemporal Fourier decomposition was applied to the TD-DOI data to extract image components, which were grouped into datasets based on the physiological parameter that was altered. Component sensitivity to each physiological parameter was assessed using the ratio between a component’s range and standard deviation within each dataset (range-to-error ratio;RER). Mean RER was highest for changes in synovial membrane and fluid volume, suggesting that this parameter may be a primary source of contrast for monitoring RA treatment response with TD-DOI. © 2023 SPIE.

关键词： Phantoms

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Comparative Analysis of Photoplethysmogram (PPG) Waveform Characteristics Across Various Body Sites Under Normal and Apneic Conditions

Comparative Analysis of Photoplethysmogram (PPG) Waveform Ch...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Joe Rahme Sahera Saleh Tamara Al-Sadek Jason Amatoury Massoud Khraiche Biomedical Engineering Program American University of Beirut Beirut Lebanon Department of Electrical Engineering American University of Beirut Beirut Lebanon

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Detecting sleep apnea through wearable devices poses challenges due to the condition's variability across populations and the inconsistencies in measurements attributed to current wearable technologies. This study aims at comparing photoplethysmogram (PPG) waveform characteristics in healthy subjects, including the change in amplitude, width, and time to peak (Tp) of the signal. PPG signals were recorded at six different body sites (wrist upper, wrist lower, ring finger, thumb, neck, and head) under both simulated normal and apneic conditions. A key objective of this work was to identify optimal LED intensities for detecting these waveform features at each site, providing valuable insights for future development of PPG hardware by pinpointing the most effective intensities. Additionally, the research aims for a better understanding of the variation of the PPG waveform between different body sites.

关键词： Wrist Thumb Light emitting diodes Feature extraction Sleep apnea Magnetic heads Neck Object recognition Wearable devices Motion artifacts

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A VAE-Based Data Augmentation Method for Fall Detection System using Wearable Sensor 10

A VAE-Based Data Augmentation Method for Fall Detection Syst...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Tu, Yu-Chen Wang, Hsuan-Chih Liu, Chien-Pin Hsieh, Chia-Yeh Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taipei City112 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City242062 Taiwan

ISBN: (纸本)9798350394924

Fall incidents among the elderly represent a significant global concern, often resulting in physical injuries and psychological distress. It is crucial to develop reliable fall detection systems which are capable of identifying fall events immediately and triggering alerts for assistance. However, real-world fall occurrences are infrequent, leading to a highly imbalanced class situation. Training a model with imbalanced datasets may result in biased models with poor performance in fall detection. To address this challenge, various techniques such as data transformation and Synthetic Minority Oversampling Technique (SMOTE) have been proposed. However, these methods are constrained by issues such as limitations in input data size or sensitivity to outliers. Compared to other methods, variational autoencoder (VAE) can generate data with a similar probability distribution to the original input data while constraining the latent representation in a Gaussian distribution. This study proposes a VAE-based data augmentation method for wearable-based fall detection system. The proposed method is validated on the FallAllD public dataset, achieving a F-score of 99.46%. The performance has been increased by 2.21%. The results demonstrate the effectiveness of VAE-based data augmentation technique in enhancing fall detection systems and its superior performance compared with other traditional data augmentation methods. © 2024 IEEE.

关键词： Wearable sensors

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Spatial Selection Configuration on EEG Stroke Signal 1

Spatial Selection Configuration on EEG Stroke Signal

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1st International Conference on Information System and Information Technology, ICISIT 2022

作者： Firdaus, Fulki Hapsari, Anggit Fauzi, Hilman Shapiai, Ibrahim Fuadah, Yunendah Telkom University Telecommunication Engineering Study Program Faculty of Electrical Engineering Bandung Indonesia Telkom University Biomedical Engineering Study Program Faculty of Electrical Engineering Bandung Indonesia Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Electrical System Engineering Kuala Lumpur Malaysia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Stroke is one of the cerebrovascular health disorders caused by a blockage of blood flow to the brain. Data from South East Asian Medical Information Center (SEAMIC) explain that the most significant stroke mortality occurred in Indonesia, Philippines, Singapore, Brunei, Malaysia, and Thailand. There are several methods for diagnosing stroke, one of which is an electroencephalograph (EEG). EEG is one of the more widely used BCI methods due to its lower price, portability, ease of use, and high temporal resolution than other methods. Most EEG require signals from various places on the scalp to achieve good performance. However, using a large number of channels can degrade signal performance in the EEG. Spatial selection can be used for channel selection in the EEG stroke signal which can then be useful for evaluation of stroke therapy. Therefore, this study will select the optimized channel using the spatial selection method to see which channels are relevant to the EEG stroke signal. Also, using the Power Spectral Density extraction feature and Extreme Learning Machine classification. The L2-norm energy calculation method gets better results than other methods. This method can also select the active channel relevant to the stroke EEG signal. The results show that the spatial selection method can increase accuracy by 15 percent and optimize the system with 37.5 percent channel reductions. © 2022 IEEE.

关键词： Learning systems

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Intrinsic Stochastic Fluorescence Spectra and Photophysical Parameters of Nucleic Acids under Visible Light Illumination

Intrinsic Stochastic Fluorescence Spectra and Photophysical ...

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Optics and the Brain, BRAIN 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Gong, Ruyi Almassalha, Luay Backman, Vadim Department of Biomedical Engineering Northwestern University EvanstonIL60208 United States Medical Scientist Training Program Feinberg School of Medicine Northwestern University EvanstonIL60611 United States

The spectra and photophysical parameters of DNA molecules under visible light illumination of different wavelengths are measured for the first time, which may lead to a new label-free super-resolution imaging techniqu... 详细信息

ISBN: (纸本)9781957171210

关键词： Emission Illumination Imaging techniques Optical microscopy Spectroscopy Visible light

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Methodology for the Validation of Competencies and Skills Associated with the Safe Use of biomedical Technology at the University Hospital Fundación Valle Del Lili 3

Methodology for the Validation of Competencies and Skills As...

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3rd International Congress of biomedical engineering and Bioengineering, CIIBBI 2024

作者： De La Cruz Lopez, Daniel Ernesto Medina, Stephanie Ordonez Gaviria, Lorena Ortiz Biomedical Engineering Program Universidad Autónoma de Occidente Valle del Cauca Santiago de Cali Colombia Hospital Universitario Fundación Valle Del Lili Valle del Cauca Santiago de Cali Colombia

ISBN: (纸本)9798331532352

This project presents the development of a methodology for validating competencies and skills associated with the safe use of biomedical technology for healthcare personnel at the University Hospital Fundación Valle del Lili. In this project, a methodology was developed for validating the competencies and skills associated with the safe use of biomedical technology by medical and healthcare personnel at the University Hospital Fundación Valle del Lili. The aim was to strengthen the training program, contribute to patient safety, and reduce the incidence of reports associated with the incorrectly use of biomedical equipment. Initially, a diagnosis and analysis of the training program were carried out. Based on the findings, the most appropriate method for evaluating the impact of training on medical and healthcare personnel was determined, and the methodology was designed. Subsequently, the biomedical equipment and personnel profiles were selected through statistical sampling, and the methodology was applied in a pilot test. Finally, the results were shared with the institution, and a procedure was developed to implement the methodology as an institutional policy. © 2024 IEEE.

关键词： Diagnosis

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Influence of multitasking-induced mental workload on electrodermal activity and human cortisol 15

Influence of multitasking-induced mental workload on electro...

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15th biomedical engineering International Conference, BMEiCON 2023

作者： Pidech, Jutamas Neubert, Manida Swangnetr Chuchuen, Oranat Khon Kaen University Faculty of Engineering Biomedical Engineering Program Khon Kaen Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Chulalongkorn University Human-Robot Collaboration and Systems Integration Research Unit Bangkok Thailand Khon Kaen University Faculty of Engineering Department of Chemical Engineering Khon Khon Kaen Thailand

ISBN: (纸本)9798350345247

Current work environments have undergone a noticeable shift towards higher cognitive demands and increased involvement in multitask performance. It has therefore become crucial to investigate the levels of mental workload experienced by operators. Simulated cognitive multitasking, with varying workload intensity, duration and work order, was tested in 18 volunteers to determine its impact on task performance, perceived workload, galvanic skin response;GSR, and cortisol levels in sweat and saliva. The results showed that as workload intensity increased, there was a concurrent increase in perceived workload and a significant negative impact on task performance. As time progressed, there was a notable increase in performance. Differences in GSR can be observed based on time on task and order of stressor levels delivered to the participants. Preliminary results of cortisol levels in sweat and saliva showed distinct trends in relation to workload intensity. © 2023 IEEE.

关键词： Multitasking

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CNN-LSTM Proposal for Colombian Sign Language Greetings Classification: Preliminary Results 3

CNN-LSTM Proposal for Colombian Sign Language Greetings Clas...

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3rd International Congress of biomedical engineering and Bioengineering, CIIBBI 2024

作者： Rojas, Ivette Sierra, Katherin Tocora, Angelica Maria Rojas Calderon, Luis Buitrago, Natalia Orjuela-Canon, Alvaro D. Biomedical Engineering Program Universidad Del Rosario - Escuela Colombiana de Ingeniería Julio Garavito D.C. Bogota Colombia School of Medicine and Health Sciences Universidad Del Rosario D.C. Bogota Colombia

ISBN: (纸本)9798331532352

Colombian sign language (CSL) have shown different advances to automatize the translation and communication between deaf or disabling hearing people and speakers. However, these current efforts are oriented on classification of static signs, mainly. The present proposal implements the use a convolutional neural network (CNN) that also involves the use an architecture of the long short-term memory (LSTM) one. This allowed to include the time dependent information for dynamic signs like a sequence data. For this, a dataset was created with greetings for the CSL through the recording videos of signs. The methodology used the MediaPipe tool for feature extraction, which was used to represent relevant points from fingers articulation for frames of the videos. CNN-LSTM was applied with the joint points information for the classification. Results show that 71.50 % (+/-4.33%) for five greetings classification, employing a crossvalidation and early stopping criteria for the training of models. © 2024 IEEE.

关键词： Convolutional neural networks

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Spatio-Temporal Deep Learning for Final Infarct Prediction Using Acute Stroke CT Perfusion Data 4th

Spatio-Temporal Deep Learning for Final Infarct Prediction U...

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4th International Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th International Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Amador, Kimberly Winder, Anthony J. Forkert, Nils D. Biomedical Engineering Graduate Program University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Alberta Children’s Hospital Research Institute University of Calgary Calgary Canada

ISBN: (纸本)9783031811005

Accurate prediction of the tissue outcome is crucial for guiding treatment decisions in acute ischemic stroke (AIS). Spatio-temporal (4D) Computed Tomography Perfusion (CTP) provides detailed insights into cerebral blood flow dynamics, which are essential for predicting final infarct regions. However, its high-dimensional and noisy nature presents challenges for direct prediction. In this study, we evaluate a deep learning model that fully leverages 4D CTP data for predicting tissue outcomes. The model integrates a shared-weight convolutional neural network (CNN) encoder, a Transformer encoder, and a CNN decoder to capture both spatial and temporal dependencies within the data. We evaluated this approach on a multicenter dataset of 143 patients from the ISLES 2024 challenge. The results reveal a Dice score of 0.20, an absolute volume difference of 17 ml, a mean lesion count difference of 19, and a lesion-wise F1-Score of 0.02, underscoring both the potential and challenges of directly utilizing 4D CTP data for final infarct prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Deep-Learning Approach for An Analysis of Real-estate Prices and Transactions

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IEEE Access 2025年 13卷 89248-89265页

作者： Yang, Cheng-Hong Lee, Borcy Lin, Yu-Da Tainan University of Technology Department of Information Management Tainan Taiwan National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung Taiwan Kaohsiung Medical University Ph.D. Program in Biomedical Engineering Kaohsiung Taiwan Kaohsiung Medical University School of Dentistry Kaohsiung Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Kaohsiung Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced b

关键词： Houses

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