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

Spatiotemporal response analysis to simple and complex stimuli in patients with unilateral spatial neglect: 3D verification using immersive virtual reality.

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/EMBC53108.2024.10782125

Akira Koshino, Tomoki Akatsuka, Kazuhiro Yasuda, Saki Takazawa, Shuntaro Kawaguchi, Hiroyasu Iwata

{"title":"Spatiotemporal response analysis to simple and complex stimuli in patients with unilateral spatial neglect: 3D verification using immersive virtual reality.","authors":"Akira Koshino, Tomoki Akatsuka, Kazuhiro Yasuda, Saki Takazawa, Shuntaro Kawaguchi, Hiroyasu Iwata","doi":"10.1109/EMBC53108.2024.10782125","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782125","url":null,"abstract":"Unilateral spatial neglect (USN) occurs as a sequela of stroke. This study proposes a neglect-identification system to evaluate the ability of patients with USN to process higher-order information. The measurement is done by varying the complexity of stimuli presented in an immersive virtual-reality space. Clinical study was conducted on three patients with USN using the new system, and the results showed that the USN patients were able to recognize simple presented objects, but neglected complex presented objects on the neglected side. The difference in reaction time between complex and simple presented objects was compared, and it was found that there was a delay in the neglected side, assumed to be a delay in higher-order information processing. The time lapse from stimulus presentation to recognition is divided into search and recognition time, and the cause of the degradation in higher-order information processing is clarified based on eye movement during recognition time. Furthermore, quantifying the ability to process high-order information using the proposed higher-order information-processing (HoIP) index shows that this ability deteriorates spatially and in the neglected area.Clinical Relevance- The system developed in this study should provide efficient rehabilitation for each patient because it can evaluate the patient's ability to process higher-order information in a three-dimensional space.","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":"143559959","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

Validation of the estimated Effect of Ankle Foot Orthoses on Spinal Cord Injury Gait Using Subject-Adjusted Musculoskeletal Models.

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/EMBC53108.2024.10782279

Sergio Galindo-Leon, Inge Eriks-Hoogland, Kenji Suzuki, Diego Paez-Granados

{"title":"Validation of the estimated Effect of Ankle Foot Orthoses on Spinal Cord Injury Gait Using Subject-Adjusted Musculoskeletal Models.","authors":"Sergio Galindo-Leon, Inge Eriks-Hoogland, Kenji Suzuki, Diego Paez-Granados","doi":"10.1109/EMBC53108.2024.10782279","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782279","url":null,"abstract":"Simulation of assistive devices on pathological gait through musculoskeletal models offers the potential and advantages of estimating the effect of the device in several biomechanical variables and the device characteristics ahead of manufacturing. In this study, we introduce a novel musculoskeletal modelling approach to simulate the biomechanical impact of ankle foot orthoses (AFO) on gait in individuals with spinal cord injury (SCI). Leveraging data from the Swiss Paraplegic Center, we constructed anatomically and muscularly scaled models for SCI-AFO users, aiming to predict changes in gait kinematics and kinetics. The importance of this work lies in its potential to enhance rehabilitation strategies and improve quality of life by enabling the pre-manufacturing assessment of assistive devices. Despite the application of musculoskeletal models in simulating walking aids effects in other conditions, no predictive model currently exists for SCI gait. Evaluation through RMSE showed similar results compared with other pathologies, simulation errors ranged between 0.23 to 2.3 degrees in kinematics. Moreover, the model was able to capture ankle joint muscular asymmetries and predict symmetry improvements with AFO use. However, the simulation did not reveal all the AFO effects, indicating a need for more personalized model parameters and optimized muscle activation to fully replicate orthosis effects on SCI gait.","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":"143560124","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

Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning.

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/EMBC53108.2024.10781590

Ethan O'Connor, Emmanuel Yangue, Yu Feng, Huimin Wu, Chenang Liu

{"title":"Towards Personalized Inhalation Therapy by Correlating Chest CT Imaging and Pulmonary Function Test Features Using Machine Learning.","authors":"Ethan O'Connor, Emmanuel Yangue, Yu Feng, Huimin Wu, Chenang Liu","doi":"10.1109/EMBC53108.2024.10781590","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781590","url":null,"abstract":"Inhalation therapy is the predominant method of treatment for a variety of respiratory diseases. The effectiveness of such treatment is dependent on the accuracy of medication delivery. Thus, personalized inhalation therapy wherein inhaler designs are specifically suited to the patient's needs is highly desirable. Although computational fluid-particle dynamics (CFPD)-based simulation has demonstrated potential in advancing personalized inhalation therapy, it still requires a 3D model of the patient's respiratory system. Such a model could be constructed with computed tomography (CT) images; however, CT scans are costly and have a high risk of radiation exposure. This concern motivates this study to bridge chest CT images and pulmonary function test (PFT) data, which is noninvasive and easy to obtain. To achieve this goal, an autoencoder is leveraged to find a lower dimensional representation of the CT image; PFT data is then mapped to the encoded image using partial least squares (PLS) regression. Using the decoder in the trained autoencoder, a CT image can be reconstructed by the encoded image predicted by PFT data. This method would allow for greater accessibility to chest CT imaging without exposing patients to the potential negative effects of CT scans, significantly advancing personalized inhalation therapy for respiratory diseases. The results of preliminary experiments using a real-world dataset demonstrate promising performance with our proposed approach.","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":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560295","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

Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation.

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/EMBC53108.2024.10782077

Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao

{"title":"Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation.","authors":"Zolnamar Dorjsembe, Hsing-Kuo Pao, Furen Xiao","doi":"10.1109/EMBC53108.2024.10782077","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782077","url":null,"abstract":"This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Fréchet Inception Distance (FID) score of 78.47, compared to scores above 95.82) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6828 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.","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":"143559970","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

Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals.

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/EMBC53108.2024.10782851

Keshav Bimbraw, Jing Liu, Ye Wang, Toshiaki Koike-Akino

{"title":"Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals.","authors":"Keshav Bimbraw, Jing Liu, Ye Wang, Toshiaki Koike-Akino","doi":"10.1109/EMBC53108.2024.10782851","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782851","url":null,"abstract":"Biosignal-based hand gesture classification is an important component of effective human-machine interaction. For multimodal biosignal sensing, the modalities often face data loss due to missing channels in the data which can adversely affect the gesture classification performance. To make the classifiers robust to missing channels in the data, this paper proposes using Random Channel Ablation (RChA) during the training process. Ultrasound and force myography (FMG) data were acquired from the forearm for 12 hand gestures over 2 subjects. The resulting multimodal data had 16 total channels, 8 for each modality. The proposed method was applied to convolutional neural network architecture, and compared with baseline, imputation, and oracle methods. Using 5-fold cross-validation for the two subjects, on average, 12.2% and 24.5% improvement was observed for gesture classification with up to 4 and 8 missing channels respectively compared to the baseline. Notably, the proposed method is also robust to an increase in the number of missing channels compared to other methods. These results show the efficacy of using random channel ablation to improve classifier robustness for multimodal and multi-channel biosignal-based hand gesture classification.","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":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559977","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

An Implantable Ciliary Muscle LFP Recording and Transmitting System.

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/EMBC53108.2024.10781543

Sebastian Kaltenstadler, Bishesh Sigdel, Sven Schumayer, Raphael Steinhoff, Torsten Straser, Albrecht Rothermel

引用次数: 0

An Improvised Approach Using YOLOv3 Architecture for Digital Panoramic Teeth Recognition and Classification.

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/EMBC53108.2024.10782041

Veena Divya Krishnappa, Anand Jatti, Rajasree P M, Vidya M J, Revan Kumar Joshi, C H Renumadhavi, Padmaja K V, K N Subramanya, Adharsh Krishnamoorthy

{"title":"An Improvised Approach Using YOLOv3 Architecture for Digital Panoramic Teeth Recognition and Classification.","authors":"Veena Divya Krishnappa, Anand Jatti, Rajasree P M, Vidya M J, Revan Kumar Joshi, C H Renumadhavi, Padmaja K V, K N Subramanya, Adharsh Krishnamoorthy","doi":"10.1109/EMBC53108.2024.10782041","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782041","url":null,"abstract":"Tooth loss may occur due to a lack of access to diagnostic imaging and other dental radiographs, despite the fact that these images are vital for treating oral health issues. For better teeth recognition and classification networks, a new model based on YOLOv3 is suggested. A smaller convolution layer and architectural deepening for improved feature extraction are two examples of how the model improves upon the YOLOv3 model for better metrics. A reduction in convolution layers allows for fast recognition and the introduction of the network architecture. A validation/test dataset is used to assess the model's performance, with the help of the Radiology department at Bengaluru's DAPM RV Dental College and Ho spital.Clinical Relevance-When it comes to training artificial intelligence systems, radiologists are indispensable for producing accurate labels. These systems are vital for learning and dependable use in clinical areas. According to the research, artificial intelligence systems may one day be able to detect periodontal issues from digital Panoramic data.","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":"143558980","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

A Quantitative Ultrasound Phantom Calibration Study: Effect of Depth on Attenuation and Backscatter Properties.

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/EMBC53108.2024.10781924

Salman Jubair Jim, Alex Devlin, Farah Deeba

引用次数: 0

A Spatial Feature Extraction Method for Enhancing Upper Limb Motion Intent Prediction in EMG-PR System.

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/EMBC53108.2024.10782222

Boxing Peng, Haoshi Zhang, Xiangxin Li, Yue Zheng, Guanglin Li

{"title":"A Spatial Feature Extraction Method for Enhancing Upper Limb Motion Intent Prediction in EMG-PR System.","authors":"Boxing Peng, Haoshi Zhang, Xiangxin Li, Yue Zheng, Guanglin Li","doi":"10.1109/EMBC53108.2024.10782222","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782222","url":null,"abstract":"High-Density Surface Electromyography (HD-sEMG) enriches motion intention pattern recognition systems by providing more spatial information. Multichannel linear descriptors (MLD) could provide a comprehensive description of the overall state characteristics within the muscle regions. In this study, an MLD-based spatial feature extraction method was proposed to capture differences and correlations in various muscle regions during movement, ultimately enhancing the system's classification accuracy. The performance of the feature extraction method was compared with traditional time domain feature extraction method under various classifiers and different movement types. The results show that employing the proposed method with the spatial features improves the classification error rates of combined movements from 11.14% to 7.28%, and better adaptability for all classifiers utilized in this study, which shows the effect of utilization of spatial information in different muscle regions.","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":"143558991","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

3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.

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/EMBC53108.2024.10782006

Jiao Jiao Feng, Mao Wen Ba, Nan Li, Gang Wang

{"title":"3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.","authors":"Jiao Jiao Feng, Mao Wen Ba, Nan Li, Gang Wang","doi":"10.1109/EMBC53108.2024.10782006","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782006","url":null,"abstract":"The cognitive decline caused by Alzheimer's disease (AD) is closely related to the structural changes in the hippocampus captured by structural magnetic resonance imaging (sMRI). However, current deep model research on the morphological analysis of hippocampus is mainly based on 2D MRI slices, lacking a comprehensive description of the 3D surface morphology and complex textures of the hippocampus. For this reason, we propose a two-stream multi features deep learning model that establishes a descriptive system for 3D spatial structure and morphological atrophy features on the triangular mesh of left and right hippocampus. First, we encode the triangular mesh data into the spatial structural features of the hippocampal surface. Second, considering the tubular structure of the hippocampus and the inhomogeneous morphological changes caused by AD, we introduce the thickness features and Heat Kernel Signature (HKS) features for the morphological atrophy features encoding. Third, we integrate the encoded features of adjacent faces from a macroscopic perspective into the discriminative morphological features induced by AD. Finally, driven by classification tasks, the deep learning model parameters and the discriminative features are continuously optimized, thereby improving the accuracy of AD diagnosis. Our method is evaluated based on the T 1 weighted sMRI baseline data of 269 Aβ+ AD and 437 Aβ-normal cognitively(NC) subjects collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The classification accuracy of this method for AD and NC subjects is 93.4%, the sensitivity and specificity are 92.4% and 93.8%, respectively, and the area under the ROC curve (AUC) is 98.3%.","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":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558995","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