Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献_第6页

Robotic Assistance for Precise Spinal Injections: Development and Clinical Verification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781757

Aswathaman G, Keerthivasan S, Shyam A, Manojkumar Lakshmanan, Mohanashankar Sivaprakasam

{"title":"Robotic Assistance for Precise Spinal Injections: Development and Clinical Verification.","authors":"Aswathaman G, Keerthivasan S, Shyam A, Manojkumar Lakshmanan, Mohanashankar Sivaprakasam","doi":"10.1109/EMBC53108.2024.10781757","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781757","url":null,"abstract":"Robot-assisted surgical systems have shown promising results and better patient outcomes in pedicle screw instrumentation and percutaneous needle interventions. Many commercial robotic assistance systems are available for the aforementioned procedures. However, there is only limited literature on robotic spinal injection and needle delivery. Moreover, there is no robotic system that is commercially available for assisting surgeons in spinal injections and needle placement. To address this gap, we developed a robotic system that can provide stereotactic assistance to the surgeon for administering spinal injections and needles. The system utilizes a commercially available collaborative manipulator and a stereoscopic navigation system. A robot motion planner was developed to impart collision avoidance capabilities and make the manipulator adept for the surgical setting. A clinical phantom study was conducted to validate the overall system performance and accuracy. 60 different needle plans were targeted on the lumbar region by expert surgeons and executed through the proposed system. A mean target point error of 1.02 mm with a standard deviation of 0.5 mm was achieved. The observations and results obtained through the study show that the proposed robotic guidance system can be of potential aid in accomplishing accurate spinal needle and injection delivery.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782081

Nikola Kolbl, Konstantin Tziridis, Patrick Krauss, Achim Schilling

{"title":"Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG.","authors":"Nikola Kolbl, Konstantin Tziridis, Patrick Krauss, Achim Schilling","doi":"10.1109/EMBC53108.2024.10782081","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782081","url":null,"abstract":"Analyzing and deciphering brain signals on a single trial base is the main goal of brain-computer interface (BCI) research as well as neurolinguistics. In the present study, we have evaluated the efficacy of three neuroimaging techniques-active electroencephalography (EEG), passive EEG, and magnetoencephalography (MEG)-in capturing and evaluating brain activity in response to auditory stimuli. The main goals of our research included two primary components: first, to identify ROIs, and second, to determine the appropriate number of stimulus samples needed to achieve a meaningful level of reliability. To estimate this number of measurement repetitions we performed step-wise sub-sampling combined with permutation testing. This involved a detailed comparison of event-related potentials resp. fields (ERPs, ERFs) elicited by auditory stimuli such as acoustic clicks and continuous speech. Our results show that active EEG outperformed passive EEG and MEG in sensor space. However, MEG demonstrated superior signal localization in source space. These results also highlight the complexity of developing real-time speech BCIs.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

METAVEST: Liquid Metal Biomimetic Personal Cooling System for Industry Workers.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782241

M Arifur Rahman, Mohammad Uzzaman, Radwa Elshenawy, Wedyan Babatain

{"title":"METAVEST: Liquid Metal Biomimetic Personal Cooling System for Industry Workers.","authors":"M Arifur Rahman, Mohammad Uzzaman, Radwa Elshenawy, Wedyan Babatain","doi":"10.1109/EMBC53108.2024.10782241","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782241","url":null,"abstract":"Hot environments can negatively impact worker health, well-being, and productivity, especially in industrial and outdoor settings. Personal cooling systems (PCS) provide a solution, but current systems have limitations in cooling capacity, size, mobility, and battery life. This study introduces METAVEST, a lightweight and energy-efficient PCS comprising a cooling unit and a biomimetic vest. It utilizes Galinstan as the primary coolant and ice as the secondary coolant. The Galinstan circulates through tubing attached to the vest, absorbing body heat and cooling via tubing embedded in an insulated cold pack with ice. This study focuses on designing a cooling vest tubing network, which draws inspiration from human heart capillaries for efficient heat transfer. Rectangular-shaped thermally conductive tubing is fabricated and characterized for efficient heat transfer from the body, and its flow resistance and heat transfer characteristics are compared with circular tubing. Additionally, a network of tubing and a prototype vest has been developed to mitigate heat risks for industry workers in hot conditions, ensuring their safety and improving performance by addressing heat-related challenges.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sleep structure discriminates patients with isolated REM sleep behavior disorder: a deep learning approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782600

Simon Feuerstein, Ambra Stefani, Raphael Angerbauer, Kristin Egger, Abubaker Ibrahim, Evi Holzknecht, Birgit Hogl, Antonio Rodriguez-Sanchez, Matteo Cesari

{"title":"Sleep structure discriminates patients with isolated REM sleep behavior disorder: a deep learning approach.","authors":"Simon Feuerstein, Ambra Stefani, Raphael Angerbauer, Kristin Egger, Abubaker Ibrahim, Evi Holzknecht, Birgit Hogl, Antonio Rodriguez-Sanchez, Matteo Cesari","doi":"10.1109/EMBC53108.2024.10782600","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782600","url":null,"abstract":"Rapid eye movement (REM) sleep behavior disorder (RBD) is a disorder characterized by increased muscle tone and dream-enactment behaviors in REM sleep. In its isolated form (iRBD), it is a prodromal stage of neurodegenerative diseases. Currently, diagnosis of RBD requires time-consuming and subjective visual inspection of polysomnography (PSG). We propose a novel fast and objective deep learning model to identify patients with iRBD based on their sleep structure. A total of 86 iRBD and 81 controls, who underwent PSG, were included in the study. A validated algorithm was used to generate hypnodensity graphs (i.e., probabilistic representations of sleep structure). A ResNet-18 model was trained on five datasets consisting of whole night hypnodensities (with and without augmentation), and shorter segments (4 hours, 2 hours, and 30 minutes) to discriminate iRBD from controls. Using entire-night hypnodensity had notable benefits in terms of performance compared to shorter length segments, leading to a mean macro F1 score of 0.717 (per-segment), and of 0.784 (per-subject). Our findings show that sleep structure is important for iRBD classification and could potentially help clinicians.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782435

Sudipta Paul, Bulent Yener, Amanda W Lund

{"title":"C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading.","authors":"Sudipta Paul, Bulent Yener, Amanda W Lund","doi":"10.1109/EMBC53108.2024.10782435","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782435","url":null,"abstract":"Graph-based learning approaches, due to their ability to encode tissue/organ structure information, are increasingly favored for grading colorectal cancer histology images. Recent graph-based techniques involve dividing whole slide images (WSIs) into smaller or medium-sized patches, and then building graphs on each patch for direct use in training. This method, however, fails to capture the tissue structure information present in an entire WSI and relies on training from a significantly large dataset of image patches. In this paper, we propose a novel cell-to-patch graph convolutional network (C2P-GCN), which is a two-stage graph formation-based approach. In the first stage, it forms a patch-level graph based on the cell organization on each patch of a WSI. In the second stage, it forms an image-level graph based on a similarity measure between patches of a WSI considering each patch as a node of a graph. This graph representation is then fed into a multi-layer GCN-based classification network. Our approach, through its dual-phase graph construction, effectively gathers local structural details from individual patches and establishes a meaningful connection among all patches across a WSI. As C2P-GCN integrates the structural data of an entire WSI into a single graph, it allows our model to work with significantly fewer training data compared to the latest models for colorectal cancer. Experimental validation of C2P-GCN on two distinct colorectal cancer datasets demonstrates the effectiveness of our method.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Camera-based Gait Kinematic Features Analysis and Recognition of Autism Spectrum Disorder.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782497

Minghao Du, Tao Li, Yunuo Xu, Peng Fang, Xin Xu, Ping Shi, Wei Liu, Xiaoya Liu, Shuang Liu

{"title":"Camera-based Gait Kinematic Features Analysis and Recognition of Autism Spectrum Disorder.","authors":"Minghao Du, Tao Li, Yunuo Xu, Peng Fang, Xin Xu, Ping Shi, Wei Liu, Xiaoya Liu, Shuang Liu","doi":"10.1109/EMBC53108.2024.10782497","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782497","url":null,"abstract":"The atypical development in children with autism spectrum disorder (ASD) may cause varying degrees of gait deficits, characterized by uncoordinated and peculiar postures. However, these symptoms are often ignored due to their subtlety. This study aimed to quantify the atypical gait pattern in ASD and explore the feasibility of a gait-based method for ASD recognition. Firstly, we collected natural walking videos from 38 ASD children and 30 health control (HC) children, then extracted gait kinematic parameters using a skeleton model, including joint swing angle and amplitude features, to analyze subtle changes among ASD children. Subsequently, the potential correlation of these features with the clinical severity of ASD was analyzed, and several machine learning models were constructed for recognition. The results showed, compared to HC group, ASD group had a significant decrease in step length, speed, leg swing angle and coordination, along with a significant increase in head angle. Moreover, significant correlations were observed between these features and both Autism Behavior Checklist (ABC) and Clancy Autism Behavior Scale scores, except for the coordination, which only exhibited significant correlation with ABC score. For recognition, the Random Forests achieved the best recognition performance with an accuracy of 0.84 and an F1 score of 0.86. Overall, this study reveals the atypical gait pattern of ASD children, and proposes a novel gait-based recognition model for future auxiliary evaluation.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can Camera-PPG Imaging be Used to Measure Perfusion Index?

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781667

Zhiyuan Xu, Yukai Huang, Ningbo Zhao, Jia Huang, Hongzhou Lu, Wenjin Wang

引用次数: 0

Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782966

Samuel Ruiperez-Campillo, Alain Ryser, Thomas M Sutter, Ruibin Feng, Prasanth Ganesan, Brototo Deb, Kelly A Brennan, Maarten Z H Kolk, Fleur V Y Tjong, Albert J Rogers, Sanjiv M Narayan, Julia E Vogt

{"title":"Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy.","authors":"Samuel Ruiperez-Campillo, Alain Ryser, Thomas M Sutter, Ruibin Feng, Prasanth Ganesan, Brototo Deb, Kelly A Brennan, Maarten Z H Kolk, Fleur V Y Tjong, Albert J Rogers, Sanjiv M Narayan, Julia E Vogt","doi":"10.1109/EMBC53108.2024.10782966","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782966","url":null,"abstract":"Reducing electrophysiological (EP) signal noise is essential for diagnosis, mapping, and ablation, yet traditional approaches are suboptimal. This study tests the hypothesis that generative artificial intelligence (AI), specifically Variational Autoencoders (VAEs), can effectively denoise these signals by forming robust internal representations of 'clean' signals. Utilizing a dataset of 5706 time series from 42 patients with ischemic cardiomyopathy at risk of cardiac sudden death, we set out to apply a β-VAE model to denoise and reconstruct intra-ventricular monophasic action potential (MAP) signals, which have verifiable morphology. The β-VAE model is evaluated against various noise types, including EP noise, demonstrating superior denoising performance compared to traditional methods (Pearson's Correlation of denoised vs original of 0.967 ± 0.009 for our proposed model vs 0.879 ± 0.022 for the best performing baseline). Results indicate that the model effectively reduces a wide array of noise types, particularly EP noise. We conclude that generative AI provides powerful tools that can eliminate diverse sources of noise in single beats by learning essential signal features without manual annotation, outperforming state-of-the-art denoising techniques.Clinical Relevance- The proposed β-VAE model's ability to effectively denoise and reconstruct intracardiac signals, particularly in the challenging context of arrhythmias, can significantly enhance diagnostic accuracy across a variety of heart rhythm disorders and improve treatment efficacy.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classifying Driver Distraction with Textile Electrocardiograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782613

Kaveti Pavan, Vishal Singh Roha, Tomohiko Igasaki, P A Karthick, Digvijay S Pawar, Nagarajan Ganapathy

{"title":"Classifying Driver Distraction with Textile Electrocardiograms.","authors":"Kaveti Pavan, Vishal Singh Roha, Tomohiko Igasaki, P A Karthick, Digvijay S Pawar, Nagarajan Ganapathy","doi":"10.1109/EMBC53108.2024.10782613","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782613","url":null,"abstract":"Textile sensor-based vital sign assessment plays an important role in continuous monitoring due to its unobtrusive and non-invasiveness. Textile electrocardiography (ECG) sensors allow mental wellbeing assessments in drivers during driving. In this study, we assess the effectiveness of a single-lead ECG obtained from a non-medical-grade ECG shirt for detecting driver distraction due to induced stress. Using ECG shirts, a single-lead ECG (256Hz, 12 bits) is acquired from N=10 healthy volunteers having driving licenses in three distinct driving situations (Baseline, Texting, Calling) in a controlled environment. ECG data is manually checked, and segmented into short durations (10, 30, 60 seconds). These segments are applied to a customized convolution neural network (ccNN). The proposed approach is able to classify the driver's distraction with ccNN yielding a weighted F-Score of 0.65 and an average accuracy of 67.12% on the validation set. Leave-One-Subject-Out Cross-Validation results showed weighted F-Scores ranging from 0.53 to 0.75. Thus, a single-lead, wearable textile ECG provides informative insights into a driver's mental wellbeing.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clean Synthesis of ZnO-Au Nanoconjugate Inks for Bandgap Tuning Applications.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782882

Harikrishnan Muraleedharan Jalajamony, Soumadeep De, Renny Edwin Fernandez

引用次数: 0