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Convolutional neural network-based fast seizure detection from video electroencephalograms

biomedical Signal Processing and Control 2023年 80卷

作者： Chou, Chi-Hsiang Shen, Tsu-Wang Tung, Hsin Hsieh, Peiyuan F. Kuo, Chih-En Chen, Ting-Mao Yang, Chao-Wei Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan The Department of Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Department of Neurology National Yang Ming Chiao Tung University School of Medicine Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Master's Program in Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan

Objective: Ictal stage detection in electroencephalography (EEG) is important for epilepsy diagnosis. However, it's a laborious and time-consuming task for neurologists, especially with lengthy EEG recordings (such as video-EEG). Here we applied the current deep learning techniques to improve ictal stage detection efficiency. Methods: We enrolled epileptic patients at video-EEG examination beds of our neurology department between Jan 2016 and Apr 2020. Four stages of EEG (interictal, preictal, ictal and postictal) were labeled and confirmed by neurologists. Labeled EEG signals were transformed into second-order Poincaré difference plots. The whole dataset was divided into ‘Training’, ‘Validation’ and ‘Testing’ datasets with the proportions of 45%, 5% and 50% respectively. We applied 4 Convolutional Neural Networks (CNNs), namely, AlexNet, GoogLeNet, ResNet50 and DenseNet201, to learn classifying. The top three CNNs were put into an ensemble model for datasets classification. Results: 30 epileptic patients were included: Male/Female 16/14, mean age 42.8 ± 15.0 (range 22 ∼ 81) years old. Total recorded ictal duration was 47,444 s. Overall accuracies for each of the 4 CNN models (AlexNet, GoogLeNet, ResNet50 and DenseNet201) were 78.0%, 78.3%, 81.7% and 80.8%. We omitted the AlexNet, the other 3 CNNs were put into the ensemble model. The best performance was under test with 1% of the Testing dataset yielded the overall accuracies of 85.9%. The ictal stage accuracy was 97.7%. Conclusion: Our method is a promising computer-aid deep learning approach to detect seizures on video EEG. Significance: For testing phase, selected proportion of testing dataset may influence the performances of deep learning models. © 2022 The Author(s)

关键词： Electroencephalography

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Amelioration of the brain structural connectivity is accompanied with changes of gut microbiota in a tuberous sclerosis complex mouse model

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Translational Psychiatry 2024年第1期14卷 68页

作者： Hsieh, Christine Chin-jung Lo, Yu-Chun Wang, Hsin-Hui Shen, Hsin-Ying Chen, You-Yin Lee, Yi-Chao Biomedical Translation Research Center Academia Sinica Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan Neuroscience Research Center Taipei Medical University Taipei Taiwan Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei Taiwan International Master Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Tuberous sclerosis complex (TSC) is a genetic disease that causes benign tumors and dysfunctions in many organs, including the brain. Aside from the brain malformations, many individuals with TSC exhibit neuropsychiatric symptoms. Among these symptoms, autism spectrum disorder (ASD) is one of the most common co-morbidities, affecting up to 60% of the population. Past neuroimaging studies strongly suggested that the impairments in brain connectivity contribute to ASD, whether or not TSC-related. Specifically, the tract-based diffusion tensor imaging (DTI) analysis provides information on the fiber integrity and has been used to study the neuropathological changes in the white matter of TSC patients with ASD symptoms. In our previous study, curcumin, a diet-derived mTOR inhibitor has been shown to effectively mitigate learning and memory deficits and anxiety-like behavior in Tsc2 ^+/− mice via inhibiting astroglial proliferation. Recently, gut microbiota, which is greatly influenced by the diet, has been considered to play an important role in regulating several components of the central nervous system, including glial functions. In this study, we showed that the abnormal social behavior in the Tsc2 ^+/− mice can be ameliorated by the dietary curcumin treatment. Second, using tract-based DTI analysis, we found that the Tsc2 ^+/− mice exhibited altered fractional anisotropy, axial and radial diffusivities of axonal bundles connecting the prefrontal cortex, nucleus accumbens, hypothalamus, and amygdala, indicating a decreased brain network. Third, the dietary curcumin treatment improved the DTI metrics, in accordance with changes in the gut microbiota composition. At the bacterial phylum level, we showed that the abundances of Actinobacteria, Verrucomicrobia, and Tenericutes were significantly correlated with the DTI metrics FA, AD, and RD, respectively. Finally, we revealed that the expression of myelin-associated proteins, myelin bassic p

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Self-driven directional polypyrrole-polyethyleneimine nanopigment-infused medical textiles with potential asymmetrical ionic convection and biological effects for follicle restoration

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Journal of Polymer Research 2023年第11期30卷 403-403页

作者： Chuang, Sih-Chi Yu, Shih-An Hung, Pei-Chia Chiang, Chih-Wei Su, Wen-Ta Jheng, Pei-Ru Huang, Wei-Yung Hao, Ping-Chien Liang, Jia Wei Lin, Yung-Wei Fang, Hsu-Wei Chuang, Er-Yuan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei10608 Taiwan Hsuan Chen Technology Co. Ltd. New Taipei22055 Taiwan Bone and Joint Research Center Department of Orthopedics Taipei Medical University Hospital School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei Medical University Taipei11031 Taiwan Department of Urology Wan Fang Hospital Taipei Medical University Taipei11696 Taiwan Cell Physiology and Molecular Image Research Center Taipei Medical University-Wan Fang Hospital Taipei11696 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

Hair loss is one of the most common and distressing conditions that can be traumatic for an individual. Many non-pharmacological and pharmacological treatments for hair loss have been developed. However, serious side effects or limitations in terms of the hair regeneration potential still exist. One of the non-pharmacological treatments, biophysical stimulation, has shown promise in hair regeneration. Nevertheless, the associated discomfort and the need for external components such as an electrical power supply pose challenges to its widespread use. In this study, we present an innovative cationic fabric that utilizes repeatable asymmetrical ionic flow of a physiological fluid to internally stimulate hair follicles and skin. Characterization, ultrasonic, biochemical, and histological experiments were conducted. The use of this developed treatment significantly increased expressions of heat shock proteins and potentially improved immunoregulation in the skin, as well as increased the number of hair follicles in skin defect animals, with no adverse risks. © 2023, The Polymer Society, Taipei.

关键词： Proteins

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

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Journal of Physics: Conference Series 2017年第1期820卷

作者： H A Adhi S K Wijaya Prawito C Badri M Rezal 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.

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Generating Lymphoma Variability Through Adversarial Generation Models: A Limited Resource Approach for Synthetic Histopathology Image Generation and Classification

SSRN

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SSRN 2024年

作者： Tiwari, Ashutosh Saudarshan, Harsh Krisnawati, Dyah Ika Khafid, Muhamad Wardani, Erika Martining Mutalik, Chinmaya Chen, Chi-Long Kuo, Tsung-Rong International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495509 India Department of Nursing Faculty of Nursing and Midwifery Universitas Nahdlatul Ulama Surabaya East Java Surabaya60237 Indonesia Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Pathology School of Medicine College of Medicine and Taipei Medical University Hospital Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan Tulane University United States Graduate Institute of Clinical Medicine Taiwan

Lymphomas, a diverse group of blood cancers originating from the lymphatic system, present significant challenges for diagnosis and treatment due to their morphological and genetic differences. These variability obscures the development of personalized therapies and impacts disease outcomes. The scarcity of high-quality, diverse images of lymphoma cells is further hindered by patient privacy concerns, the rarity of certain types, and logistical challenges of collecting and processing biomedical images. Additionally, the costs and technical difficulties of microscopic imaging limit the scope of research. This paper addresses these challenges by utilizing generative adversarial networks (GANs) to generate synthetic yet realistic images of lymphoma histopathology and classify them in a low resource setting. Focusing specifically on chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), we employed GANs to enhance the understanding of lymphoma variability and support advancements in digital cancer biology and pathology. This dual approach of image generation and classification aims to improve disease diagnosis and prognosis, ultimately contributing to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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Centrifugal Spinning of Porous Polylactic Acid Fibers by Centrifugal Spinning with Phase Separation for Oil Removal Application

SSRN

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SSRN 2024年

作者： Negoro, Masaki Doan, Hoan Ngoc Vo, Phu Phong Khuat, Khanh Van Thi Kinashi, Kenji Sakai, Wataru Tsutsumi, Naoto Master’s Program of Innovative Materials Kyoto Institute of Technology Matsugasaki Sakyo Kyoto606-8585 Japan Tissue Engineering and Regenerative Medicine Laboratory School of Biomedical Engineering International University Vietnam National University Ho Chi Minh City Viet Nam Faculty of Chemistry University of Science Ho Chi Minh City Viet Nam Doctor’s Program of Materials Chemistry Graduate School of Science and Technology Kyoto Institute of Technology Matsugasaki Sakyo Kyoto606-8585 Japan Faculty of Materials Science and Engineering Kyoto Institute of Technology Matsugasaki Sakyo Kyoto606-8585 Japan

Developing biodegradable oil absorption fibrous materials is essential because discarding conventional separation materials could cause secondary pollution due to their nondegradable properties. In this study, highly hydrophobic and superoleophilic porous polylactic acid (PLA) fibers were prepared using centrifugal spinning combined with nonsolvent-induced phase separation. The porous structure of PLA fibers was controlled by the ratio between good solvent and nonsolvent in the spinning solutions. The morphology and physical properties of the obtained PLA fibers were investigated using various measurement techniques. The PLA fibrous membrane prepared from a PLA solution containing chloroform/dimethylformamide (80/20 w/w) indicated not only a high water contact angle but also a high oil sorption value. The porous PLA fibers are a potential candidate for use as green materials for environmental applications. © 2024, The Authors. All rights reserved.

关键词： Spinning (fibers)

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Peripheral pulse harmonic analysis and its clinical application: A systematic review

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Journal of Traditional and Complementary Medicine 2024年第2期14卷 223-236页

作者： Hsiao, Kun-Yuan Kang, Jiunn-Horng Wu, Yeong-Shing Chang, Hen-Hong Yang, Cheng-Ta Institute of Allied Health Sciences National Cheng Kung University Tainan Taiwan Department of Chinese Medicine Cishan Hospital Ministry of Health and Welfare Kaohsiung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Integrated Medicine College of Chinese Medicine and Chinese Medicine Research Center China Medical University Taichung Taiwan Department of Chinese Medicine China Medical University Hospital Taichung Taiwan Department of Psychology National Cheng Kung University Tainan Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Graduate Institute of Health and Biotechnology Law Taipei Medical University Taipei City Taiwan

Introduction: Pulse harmonic analysis is a quantitative and objective methodology within traditional Chinese medicine (TCM) used to evaluate pulse characteristics. However, interpreting pulse wave data is challenging due to its inherent complexity. This study aims to provide a comprehensive review and comparison of existing human pulse wave harmonic analysis methods to elucidate their patterns and characteristics. Methods: A systematic review of clinical research reports published from 1990 to 2021 was conducted, focusing on variations in harmonic characteristics across different medical conditions and physiological states. Keyword searches included terms related to analysis methods (e.g., “Pulse Spectrum,” "harmonic analysis," “harmonic index”) and measured indicators (e.g., “vascular response,” "PPG," “Photoplethysmography,” "aortic," “arterial,” "blood pressure"). Supplementary research using PubMed's Mesh terms specifically targeted “Pulse wave analysis” within the methods and statistical analysis domain. Articles were filtered based on predefined criteria, including human participants and research related to pulse pressure or vascular volume changes. Conference papers, animal studies, and irrelevant research were excluded, with literature evaluation scales selected based on the retrieved research reports. Results: Initially, 6487 research reports were identified, and after screening, 50 reports were included in the review. The analysis revealed that low-frequency harmonics increase following vigorous activity or sympathetic excitation but decrease during rest or parasympathetic excitation. Cardiovascular patients exhibited elevated first harmonics associated with the liver meridian, while diabetes patients displayed weakened third harmonics related to the spleen meridian. Liver dysfunction was linked to changes in the first harmonic, and cancer patients showed signs of liver and kidney yin deficiency in the first and second harmonics. These findings underscore

关键词： Diagnostic tools Harmonic characteristics Pulse diagnosis Pulse harmonic analysis Traditional Chinese medicine

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Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysis

arXiv

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arXiv 2023年

作者： Nguyen, Thanh-Huy Kha, Quang Hien Truong, Thai Ngoc Toan Lam, Ba Thinh Ngo, Ba Hung Dinh, Quang Vinh Le, Nguyen Quoc Khanh Department of Biomedical Engineering National Cheng Kung University Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taiwan Viet Nam Ho Chi Minh City International University Viet Nam Graduate School of Data Science Chonnam National University Korea Republic of Vietnamese-German University Viet Nam Research Center for Artificial Intelligence in Medicine Taipei Medical University Taiwan Professional Master Program in Artificial Intelligence in Medicine Taiwan

In recent years, many mammographic image analysis methods have been introduced for improving cancer classification tasks. Two major issues of mammogram classification tasks are leveraging multi-view mammographic information and class-imbalance handling. In the first problem, many multi-view methods have been released for concatenating features of two or more views for the training and inference stage. Having said that, most multi-view existing methods are not explainable in the meaning of feature fusion, and treat many views equally for diagnosing. Our work aims to propose a simple but novel method for enhancing examined view (main view) by leveraging low-level feature information from the auxiliary view (ipsilateral view) before learning the high-level feature that contains the cancerous features. For the second issue, we also propose a simple but novel malignant mammogram synthesis framework for upsampling minor class samples. Our easy-to-implement and no-training framework has eliminated the current limitation of the CutMix algorithm which are unreliable synthesized images with random pasted patches, hard-contour problems, and domain shift problems. Our results on VinDr-Mammo and CMMD datasets show the effectiveness of our two new frameworks for both multi-view training and synthesizing mammographic images, outperforming the previous conventional methods in our experimental settings. © 2023, CC BY-NC-SA.

关键词： Mammography

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Integrated application of grafted ZnO and fungicide to control the fungicide-resistant Colletotrichum spp

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Journal of the Taiwan Institute of Chemical Engineers 2024年 155卷

作者： Chang, Tao-Ho Chen, Yi-Chi Lai, Yu-Fang Wu, Tzu-Chien Lai, Chian-Hui Hsueh, Han-Yu Chang, Pi-Fang Linda Program in Plant Health Care Academy of Circular Economy National Chung Hsing University Nantou540216 Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung402202 Taiwan Master Program for Plant Medicine and Good Agricultural Practice National Chung Hsing University Taichung402202 Taiwan Department of Material Science & Engineering National Chung Hsing University Taichung402202 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402202 Taiwan Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung807378 Taiwan Department of Plant Pathology National Chung Hsing University 402202 Taiwan

Background: The Food and Agriculture Organization has reported approximately 40 % of food loss due to damage from plant pests and diseases, including fungal infections. Continuous application of agrichemicals for controlling fungal infection cause the occurrence of fungicide resistance. Colletotrichum spp., major causal agents for strawberry anthracnose, have gained concern for fungicide resistance issues. Methods: We developed novel antifungal strategies in the present study by integrating multibranched flower-like ZnO (fZnO) and agrochemicals (trifloxystrobin). fZnO compounds were synthesised using an eco-friendly and efficient hydrothermal method. Charged polymers with steric structures, such as hydrophilic polyacrylic acid (PAA) and polyethyleneimine (PEI), were grafted onto fZnO surfaces to improve the suspension ability of the compounds. We used the grafted fZnO compounds in combination with the fungicide trifloxystrobin to evaluate the in vitro and in vivo activities. Significant findings: Grafted fZnO (80 ppm) and trifloxystrobin (100 ppm) effectively inhibited the growth of fungicide-resistant Colletotrichum spp. and limited strawberry anthracnose on leaves. PEI-grafted fZnO shows promise as an eco-friendly additive in sustainable agriculture due to its photocatalytic activity and ability to withstand environmental factors. © 2023

关键词： Zinc oxide

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Histopathology Grading Identification of Breast Cancer Based on Texture Classification Using GLCM and Neural Network Method

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Journal of Physics: Conference Series 2018年第1期1120卷

作者： Riries Rulaningtyas Agoes Santika Hyperastuty Anny Setijo Rahaju Biomedical Engineering Study Program Department of Physics Airlangga University Jl. Mulyorejo Surabaya 60115 Indonesia Biomedical Engineering Master Program Airlangga University Jl. Mulyorejo Surabaya 60115 Indonesia Department of Pathology Anatomy Medical Faculty Airlangga University Jl. Mulyorejo Surabaya 60115 Indonesia

Breast cancer is the leading type of malignant tumor which is observed in women. The effective treatment depends on its early diagnosis. The gold standard of breast cancer is histopathologic examination of cancer cells. The determination of the grading in breast cancer is determined by three factors: pleomorphic, tubular formation and cell mitosis. This paper uses pleumorfic and tubular formation pattern from breast cell histopathology images. The proposed system consists of four major steps : preprocessing, segmentation, feature extraction and classification. We use k – means clustering method for image segmentation and use Gray level Cooccurence Matrix (GLCM) for feature extraction with four features (i.e. angular second moment, contrast feature, entropy feature, and variance feature). The final step is grading classification which uses Backpropagation Neural Network. Some of important parameters will be variated in this process such as learning rate and the number of node in hidden layer. The research gives good result for the identification of breast cancer grading with 88% accuracy, 85% sensitivity, and 80% specificity.

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