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Micro-electrode array (MEA) electrophysiological signal analysis by descriptive stochastic tests of the probability density structure

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Research on biomedical engineering 2025年第2期41卷 1-13页

作者： Faria, Vinícius Naves Rezende Ramos, Marina Abadia Martinoia, Sérgio Destro-Filho, João-Batista Postgraduate Program in Electrical Engineering Universidade Federal de MG Uberlândia Brazil Postgraduate Program in Biomedical Sciences Universidade Federal de MG Uberlândia Brazil Division of Informatics Bioengineering Robotics and System Engineering Università Degli Studi Di Genova Genova Italy

Purpose: The primary objective of this study is to present, analyze, and quantitatively characterize the electrical signals obtained from neuronal cultures coupled to micro-electrode arrays (MEAs). Special attention is given to the temporal evolution of electrical activity across distinct geometric regions of the MEA devices. Methods: The proposed methodology involves signal acquisition, classical analysis based on spike and burst detection, and, critically, the characterization of the probability density structure of the signals. This is achieved through the quantification of Gaussianity—expressed as the percentage of segments non-Gaussian (PSNG)—and stationarity— expressed as the percentage of segments non-stationary (PSNE). Results: Initial analyses showed that first-order power spectral density quantifiers lacked the statistical robustness required to effectively describe the complex components of MEA signals. In contrast, PSNG and PSNE demonstrated strong potential in capturing the different stages of neuronal culture development. Cluster analysis further supported the discriminative capacity of these quantifiers. Conclusion: The phase corresponding to decreased neuronal activity and onset of cell death—a biologically critical stage—exhibited the strongest correlation among the studied variables. These results suggest a discernible biological organization and highlight the relevance of PSNG and PSNE as promising tools for assessing neuronal culture dynamics. © The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering 2025.

关键词： Cell death

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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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Analyzing Significance of Facial Features for Detection of Parkinson's Disease via Static Images 4

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

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

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

ISBN: (纸本)9798350360721

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. © 2024 IEEE.

关键词： Feature extraction

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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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Coevolution of Myoelectric Hand Control under the Tactile Interaction among Fingers and Objects

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Cyborg and Bionic Systems 2022年第1期2022卷 285-302页

作者： Yuki Kuroda Yusuke Yamanoi Shunta Togo Yinlai Jiang Hiroshi Yokoi Joint Doctoral Program for Sustainability Research Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyoJapan Department of Mechan ical and Intelligent System Engineering Graduate School of Informatics and Engineering The University of Electro Communications TokyoJapan Center for Neuroscience and Biomedical Engineering The University of Electro Communications TokyoJapan Bejing Innovation Center for Inelligent Robots and Systems BeijingChina

The usability of a prosthetic hand difers significantly from that of a real ***,the complexity of manipulation increases as the number of degrees of freedom to be controlled increases,making manipulation with biological signals extremely *** overcome this problem,users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from *** previous studies,these have been left to the operating skills of the user,which is extremely difficult to *** this study,we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user's operation *** required grasping technique is achieved by determining the correlation bet ween the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli,such as myoelectricity,sense of touch,and grasping *** agents of the 16 DOF robot hand were trained with the myoelectric signals of six participants,including one child with a congenital forearm ***,each agent could open and close the hand in response to the myoeletric stimuli and could accomplish the object pickup *** the tasks,the agents successfully identified grasping patterns suitable for practical and stable positioning of the *** addition,the agents were able to pick up the object in a similar posture regardless of the partiipant,suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

关键词： robot Objects overcome

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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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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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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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Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge Distillation 48

Prefallkd: Pre-Impact Fall Detection Via CNN-ViT Knowledge D...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chi, Tin-Han Liu, Kai-Chun Hsieh, Chia-Yeh Tsao, Yu Chan, Chia-Tai National Yang Ming Chiao Tung University Department of Biomedical Engineering Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan Fu Jen Catholic University Bachelor's Program in Medical Informatics and Innovative Applications Taiwan

ISBN: (纸本)9781728163277

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed "pre-impact fall detection systems"using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms). © 2023 IEEE.

关键词： Inertial measurement units Knowledge distillation Pre-impact fall detection Vision transformer Wearable sensors

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