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6th International Conference on biomedical and Health Informatics, ICBHI 2024

作者： Hung, Wen-Kai Sun, Ming-Hsuan Lu, Chi-Jen Chen, Yu-Xian Lin, Yue-Der Master’s Program in the Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

ISBN: (纸本)9783031863226

Cardiovascular diseases (CVD) are one of the major global health threats, causing millions of deaths each year. According to data from the World Health Organization, over 500 million people are affected by cardiovascular diseases, resulting in over 20 million deaths in 2021 alone, accounting for about one-third of the total global deaths. Invasive blood pressure measurement methods not only cause discomfort to patients but also pose a risk of infection. Meanwhile, cuffless cuff-based methods cannot provide continuous monitoring. Therefore, the purpose of this study is to develop a cuffless continuous blood pressure measurement system based on photoplethysmography (PPG). By using waveform measurement technology, we aim to provide a more comfortable and safer method for blood pressure monitoring, thereby enhancing the management of cardiovascular diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blood pressure

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Beyond the Scroll: Eye-Tracking Evaluations on the National Gallery of Indonesia’s Instagram 26th

Beyond the Scroll: Eye-Tracking Evaluations on the National ...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Sari, Desy Novita Prameswari, Clarissa Rahmi Anshori, Isa Master’s Program in Library Science Universitas Indonesia Kota Depok Indonesia Department of Library and Information Sciences Universitas Indonesia Kota Depok Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia National Gallery of Indonesia Central Jakarta Indonesia

ISBN: (纸本)9789819608676

Eye-tracking studies focused on art galleries and museums within the realm of Instagram, especially in the Indonesian context, are notably scant. This investigation elucidates the user interaction and experience of the art gallery’s Instagram account, @galerinasional. Employing a quantitative approach, we implemented eye-tracking experiments using the well-known online eye-tracking software RealEye. The study participants were followers of the National Gallery of Indonesia’s (Galnas) Instagram profile, seven women and three men, in two age brackets: 18–24 years (n = 5) and 25–34 years (n = 5). The experimental protocol required participants to execute five tasks centered on locating exhibition details and artwork on the Galnas Instagram platform, with a duration of 100 s allocated to each task. The Participant Quality Grade (PQG), examination duration, optimal browser dimensionality, K-factor mean, gaze and fixation frequencies, and heat maps were determined for the participants. The key findings are as follows. (1) The participants’ PQG scores were predominantly between 4 and 6. (2) Investigative browsing typically exceeded the 100-s limit. (3) The consensus on the ideal browser dimensions hovered around 1,368.6 × 814.8 px. (4) The K-factor mean across participants tended toward K > 0. (5) The average gaze occurrence was 27 items per second for most participants. (6) A noticeable disparity in fixation counts was observed between the two age groups, with individuals aged 25–34 demonstrating prolonged engagement with Galnas’ content. (7) Heat maps accentuated a pronounced concentration on visual content, encompassing photographs, videos, and their associated captions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Understanding age-related middle ear properties and basilar membrane damage in hearing loss: A finite element analysis and retrospective cohort study

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Computers in Biology and Medicine 2025年 184卷 109376-109376页

作者： Shih, Tzu-Ching Yu, You-Cheng Wang, Tang-Chuan Department of Biomedical Imaging and Radiological Science College of Medicine China Medical University Taichung404328 Taiwan The PhD Program for Medical Engineering and Rehabilitation Science College of Biomedical Engineering China Medical University Taichung404328 Taiwan Master Program for Biomedical Engineering College of Biomedical Engineering China Medical University Taichung404328 Taiwan Department of Otolaryngology-Head and Neck Surgery China Medical University Hsinchu Hospital Zhubei City Hsinchu302056 Taiwan

Age-related hearing loss (ARHL) is primarily attributed to inner-ear factors, yet the role of age-related middle ear characteristics remains elusive. Employing a finite element (FE) model, we conducted a comparative analysis with clinical data extracted from a retrospective cohort study involving 90 younger adults (mean age = 38.1 ± 7.7) and 111 older adults (mean age = 63.8 ± 8.4). The clinical dataset encompassed air-bone gap (ABG) measurements obtained through pure-tone audiometry (PTA) at frequencies of 0.5, 1.0, 2.0, and 4.0 kHz. FE results quantified the normalized stapes displacement value of the simulated form of air-bone gap (ABGSim) between the two age groups. The Mann-Whitney U test, with a significance threshold set at P © 2024

关键词： Bone

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A computationally efficient algorithm for estimating respiratory rate from seismocardiogram

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biomedical Signal Processing and Control 2025年 109卷

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 advancements in microelectromechanical system (MEMS) technology, monitoring respiration using seismocardiogram (SCG) has emerged 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: first, applying Gaussian averaging filter for pre-processing, followed by 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 that appears 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 demonstrates its feasibility for estimating RR from SCG signal. 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. Furthermore, an experiment using the dataset collected with a magnetic field-based respiration sensor during pulmonary rehabilitation is conducted to validate the algorithm's robustness against motion artifacts. Based on the testing results, the proposed algorithm shows significant potential in clinical and healthcare applications. © 2025 Elsevier Ltd

关键词： Gaussian distribution

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Hybrid statistical and machine-learning approach to hearing-loss identification based on an oversampling technique

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Computers in Biology and Medicine 2025年 185卷 109539-109539页

作者： Wang, Tang-Chuan Sun, Ko-Han Chih, Mingchang Chen, Wei-Chun Department of Otolaryngology-Head and Neck Surgery China Medical University Hsinchu Hospital Zhubei City Hsinchu302056 Taiwan Department of Master Program for Biomedical Engineering College of Biomedical Engineering China Medical University Taichung404328 Taiwan School of Medicine College of Medicine China Medical University Taichung404328 Taiwan Department of Business Administration National Chung Hsing University South District Taichung402202 Taiwan Stainless Steel Business Group Walsin Lihwa Corporation Wuqi District Taichung43541 Taiwan

Background and objectives: Hearing loss is a crucial global health hazard exerting considerable social and physiological effects on spoken language and cognition. Patients affected by this condition may experience social and professional hardships that dominate occupational injuries. Therefore, the identification of the features of recessive hearing loss is important for clinicians to prevent further disease progression. This work aimed to develop a hybrid statistical and machine-learning approach as a decision-support mechanism. We expect the proposed model to help predict hearing-loss disorders and support clinical diagnosis. Methods: A three-phase hybrid approach was proposed to implement classification models. A stepwise method and a random forest (RF) technique were utilized as filters during feature selection. Phase I involved reducing the number of input variables and selecting the most influential features. Phase II included the use of an oversampling technique called synthetic minority oversampling technique (SMOTE) to oversample the minority class and balance the sample size between the target and nontarget classes. Phase III focused on the final model selection based on three supervised classification models, namely, the logistic regression, multilayer perceptron, and support vector machine (SVM), for the target identification and prediction of the case of interest (i.e., hearing loss). Results: The analysis of phase I involved the selection and acquisition of three and seven features through the stepwise technique and RF method, respectively. The SMOTE technique alleviated the imbalanced data issue and improved the predictive capability substantially in phase II and III. Accordingly, in terms of accuracy, precision, recall, and F1 score, our empirical results demonstrated that the proposed hybrid approach involving the SVM method combined with a stepwise technique was competitive against the logistic model featuring all variables. Furthermore, the SVM mo

关键词： Diagnosis

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Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine 5

Automatic detection of ischemic stroke based on scaling expo...

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5th International Conference on Science and engineering in Mathematics, Chemistry and Physics 2017, ScieTech 2017

作者： Adhi, H.A. Wijaya, S.K. Prawito Badri, C. Rezal, M. Biomedical Engineering Master Program Universitas Indonesia Jakarta Indonesia Faculty of Mathematics and Natural Sciences Universitas Indonesia West Java Indonesia

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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Comparison between Body Fat Measurements Obtained by Portable Ultrasound and Caliper in Young Adults

Comparison between Body Fat Measurements Obtained by Portabl...

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

作者： Ulbricht, L. Neves, E. B. Ripka, W. L. Romaneli, E. F. R. Master Program in Biomedical Engineering Federal University of Technology - Paraná. Brazil Federal University of Technology - Paraná. Brazil Electrical Engineering and Industrial Computing Program Federal University of Technology - Paraná Brazil Control and Automation Engineering Federal University of Technology - Paraná Brazil

ISBN: (纸本)9781457717871

The objective of this study was to compare and correlate the Portable Ultra Sound (US) measuring technique to the skinfold measuring technique (SF) to estimate body fat percentage (%F) in young adults. Sixty military were evaluated, all males, divided in two groups: Group 1 (normal) composed by 30 military with Body Mass Index (BMI) until 24.99 kg/m(2) and Group 2 (overweight) composed by 30 military with BMI > 25 kg/m(2). Weight, height, skinfolds and ultrasound were measured in 9 points (triceps, subscapular, biceps, chest, medium axillary, abdominal, suprailiac, thigh and calf). Body fat average values obtained by skinfold thickness and ultrasound measurements were 13.25 +/- 6.32 % and 12.73 +/- 5.95 % respectively. Despite significant differences in measurements of each anatomical site, it was possible to verify that the total final body fat percentage calculated by both techniques did not present significant differences and that overweight group presented greater similarity between the values obtained using caliper and ultrasound equipment.

关键词： Body composition Skinfold thickness Portable Ultrasound Anthropometry Healthcare

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Spectral characteristics of microcirculatory signals response to muscle stretching among diabetic smokers 17

Spectral characteristics of microcirculatory signals respons...

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4th International Conference on biomedical and Bioinformatics engineering, ICBBE 2017

作者： Bau, Jian-Guo Lin, Yue-Der Huang, Bo-Wen Department of Biomedical Engineering Hungkuang University Taichung43302 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Master's Program of Bioinformatics and Biomedical Engineering Feng Chia University Taichung40724 Taiwan

ISBN: (纸本)9781450354844

Cigarette smoking is reported to be the major risk factor for endothelial dysfunction. The variations of the spectral compositions of microcirculatory perfusion signal could provide the information related to the regulatory function of microvasculature. The aim of the present study was to compare the spectral characteristics response to muscle stretching between diabetic smokers and non-smokers, and investigate the influence of cigarette smoking on microvascular function. Twenty four diabetic patients (12 smokers v.s. 12 non-smokers) and twelve normal non-smoking subjects were recruited. We observed that the diabetic smokers had a significantly reduced endothelial activity. The mechanisms for the reduction of the endothelial activity remain to be elucidated. The spectral analysis might be used to evaluate endothelial function for research, for diagnostic purposes, and maybe also to assess effects of therapy in cardiovascular diseases. © 2017 Association for Computing Machinery.

关键词： Spectrum analysis

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Ultrasonic interfacial crosslinking of TiO2-based nanocomposite hydrogels through thiol-norbornene reactions for sonodynamic antibacterial treatment

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Biomaterials Science 2023年第12期11卷 4184-4199页

作者： Yang, Su-Rung Wang, Reuben Yan, Chen-Jie Lin, Yi-Yun Yeh, Yu-Jia Yeh, Ying-Yu Yeh, Yi-Cheun Institute of Polymer Science and Engineering National Taiwan University Taipei Taiwan Institute of Food Safety and Health National Taiwan University Taipei Taiwan Master of Public Health Program National Taiwan University Taipei Taiwan GIP-TRIAD Master's Degree in Agro-Biomedical Science National Taiwan University Taipei Taiwan

Nanocomposite (NC) hydrogels used for sonodynamic therapy (SDT) face challenges such as lacking interfacial interactions between the polymers and nanomaterials as well as presenting uneven dispersion of nanomaterials in the hydrogel network, reducing their mechanical properties and treatment efficiency. Here, we demonstrate a promising approach of co-engineering nanomaterials and interfacial crosslinking to expand the materials construction and biomedical applications of NC hydrogels in SDT. In this work, mesoporous silica-coated titanium dioxide nanoparticles with thiolated surface functionalization (TiO2@MS-SH) are utilized as crosslinkers to react with norbornene-functionalized dextran (Nor-Dex) through ultrasound-triggered thiol-norbornene reactions, forming TiO2@MS-SH/Nor-Dex NC hydrogels. The TiO2@MS-SH nanoparticles act not only as multivalent crosslinkers to improve the mechanical properties of hydrogels under ultrasound irradiation but also as reactive oxygen species (ROS) generators to allow the use of TiO2@MS-SH/Nor-Dex NC hydrogels in SDT applications. Particularly, the TiO2@MS-SH/Nor-Dex NC hydrogels present tailorable microstructures, properties, and sonodynamic killing of bacteria through the modulation of the ultrasound frequency. Taken together, a versatile TiO2-based NC hydrogel platform prepared under ultrasonic interfacial crosslinking reactions is developed for advancing the applications in SDT. © 2023 The Royal Society of Chemistry.

关键词： Titanium dioxide

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Quantitative Assessment of Gait in Spinocerebellar Ataxia Using Deep Learning 4

Quantitative Assessment of Gait in Spinocerebellar Ataxia Us...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

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

ISBN: (纸本)9798350360721

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. © 2024 IEEE.

关键词： Gait analysis

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