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

{"title":"Assessing Basic Emotion via Machine Learning: Comparative Analysis of Number of Basic Emotions and Algorithms.","authors":"Caryn Vowles, Mackenzie Collins, T Claire Davies","doi":"10.1109/EMBC53108.2024.10782053","DOIUrl":"10.1109/EMBC53108.2024.10782053","url":null,"abstract":"<p><p>This paper explores the use of machine learning (ML) methods to identify \"clusters\" of basic emotions based on pleasure, arousal, and dominance (PAD). The data was obtained from the Dataset for Emotion Analysis using Physiological Signals (DEAP), data collected within the Building and Designing Assistive Technology (BDAT) Lab using the International Affective Picture System (IAPS), and the scores of PAD from the IAPS. The objective is to develop an algorithm that maps a PAD score to clusters that express emotions, e.g., sadness or happiness. The elbow method was used to determine the optimal number of clusters (4-8), and nine different ML algorithms were compared. Decision Trees, polynomial support vector machines (SVMs) and linear SVMs provided accurate results. The Decision Tree demonstrated efficiency, during both testing and validation, in identifying the same clusters when analyzing both the DEAP and IAPS datasets. The dataset included limited data for each emotion creating the possibility of overfitting. However, when evaluating the results relative to previous research, the results added to the understanding of the nuances of emotion self-reporting and modelling.</p>","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":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559073","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}

{"title":"Improving Early Prediction of Abnormal Recovery after Appendectomy in Children using Real-world Data from Wearables.","authors":"Rui Hua, Megan K O'Brien, Michela Carter, J Benjamin Pitt, Soyang Kwon, Hassan M K Ghomrawi, Arun Jayaraman, Fizan Abdullah","doi":"10.1109/EMBC53108.2024.10782031","DOIUrl":"10.1109/EMBC53108.2024.10782031","url":null,"abstract":"<p><p>Postoperative complications are primary concerns during early recovery from pediatric appendectomy. Identifying complications or symptoms of abnormal recovery typically relies on intermittent and subjective assessments from children and their caregivers, which may result in delayed diagnosis. Wearable devices can capture continuous and objective health measurements, which can be mined for early biomarkers of complications or symptoms using machine learning models. However, real-world datasets from wearables often have missing and imbalanced data, which can affect model performance and utility. We have recorded real-world Fitbit data from 93 children during the first 21 days following appendectomy for complicated appendicitis. This dataset included missing data (37.0% across all participants) and imbalanced data (2.7% of total days recorded from children exhibiting abnormal recovery). Aiming to improve early prediction of abnormal recovery, we extracted 143 daily features from the data, including Fitbit metrics of activity, heart rate, and sleep, as well as metrics derived from clinical knowledge. We trained a Balanced Random Forest classifier and tested different early prediction strategies for identifying abnormal recovery (complications or abnormal symptoms) 1-3 days before they were clinically diagnosed. The best-performing model predicted abnormal recovery three days before diagnosis at an accuracy of 87.5%, two days before at 76.4%, one day before at 85.7%, and on the day of diagnosis at 78.8%. The overall prediction accuracy was improved 10.1% compared to a previous study. With further development, this approach could be used to generate near real-time alerts of abnormal postoperative recovery to enhance pediatric care and clinical decision making.</p>","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":"143559623","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}

{"title":"Cellular-Resolution Image-Guided Localization in the Primate Brain.","authors":"Jacob Stefanowicz, John S Choi, Katie Wingel, Jarl Haggerty, Adam S Charles, Bijan Pesaran","doi":"10.1109/EMBC53108.2024.10782857","DOIUrl":"10.1109/EMBC53108.2024.10782857","url":null,"abstract":"<p><p>A fluorescence microscope mounted on a parallel-kinematic stage can be flexibly positioned to image brain tissue across the dorsal cortical convexity of the non-human primate. We introduce a computer-vision pipeline that enables accurate, 10-20 µm, real-time, 10-20 s, localization with reference to prior imaging sessions obtained on different days.</p>","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":"143558832","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}

{"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":"10.1109/EMBC53108.2024.10782497","url":null,"abstract":"<p><p>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.</p>","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}

{"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":"10.1109/EMBC53108.2024.10782851","url":null,"abstract":"<p><p>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.</p>","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}

{"title":"Development of an Electromechanical Biomimetic Prosthesis using 3D Printing: Initial Findings for Interphalangeal and Metacarpophalangeal Joints.","authors":"J Inan Aguilera B, Jorge Aguilar, Fabian Figueroa, Manuel Gutierrez, Britam Gomez","doi":"10.1109/EMBC53108.2024.10781836","DOIUrl":"10.1109/EMBC53108.2024.10781836","url":null,"abstract":"<p><p>This study presents the design and preliminary evaluation of a biomimetic prosthetic hand, leveraging 3D printing. Constructed using PLA for bone structures obtained from CT scans and TPU A95 for ligaments, the prosthetic's kinematics were evaluated focusing on the index finger. Controlled by DC motors, its movements were analyzed using Kinovea software and a 240 fps camera. The results showed high correlation coefficients (R² ≥ 0.92) for abduction, adduction, and phalange movements, with mean absolute errors ranging from -3.09° to 10.56°. These findings highlight the need for precise anatomical adjustments and confirm the prosthetic's efficacy in mimicking natural hand movements. This research advances the development of accessible, functional upper limb prosthetics, and underscores directions for enhancing their precision and functionality.</p>","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":"143559320","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}

{"title":"Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer's Disease Effect Detection.","authors":"Nikhil J Dhinagar, Sophia I Thomopoulos, Emily Laltoo, Paul M Thompson","doi":"10.1109/EMBC53108.2024.10782737","DOIUrl":"10.1109/EMBC53108.2024.10782737","url":null,"abstract":"<p><p>Generative AI models have recently achieved mainstream attention with the advent of powerful approaches such as SORA, DALL-E and stable diffusion. The underlying breakthrough generative mechanism of denoising diffusion modeling can generate high quality synthetic images and can learn the underlying distribution of complex, high-dimensional data. In our paper, we train conditional latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) to provide insight into Alzheimer's disease (AD) effects on the brain's anatomy at the individual level. We first created diffusion models that could generate synthetic MRIs, by training them on real 3D T1-weighted MRI scans, and conditioning the generative process on the clinical diagnosis as a context variable. We conducted experiments to overcome limitations in training dataset size, compute time and memory resources by testing different models, effects of pretraining, training duration. We tested the sampling quality of the disease-conditioned diffusion using metrics to assess realism and diversity of the generated synthetic MRIs. We also evaluated the ability of diffusion models to conditionally sample MRI brains using a 3D CNN-based disease classifier relative to real MRIs. In our experiments, the diffusion models generated synthetic data that helped to train an AD classifier (using only 500 real MRI scans) - and boosted its performance by over 3% when tested on real MRI scans. Further, we used classifier-free guidance to alter the conditioning of an encoded individual scan to its counterfactual (representing a healthy subject of the same age and sex) while preserving subject-specific image details. From this counterfactual image (where the same person appears healthy), a personalized disease map was generated to identify possible disease effects on the brain. Our approach efficiently generates realistic and diverse synthetic data, and may create interpretable AI-based maps for neuroscience research and clinical diagnostic applications.</p>","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":"143559116","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}

{"title":"Graph Representation of Postoperative Patients for Opioids Refill Prediction: A Real-World Case Study.","authors":"Ashok Choudhary, Cornelius A Thiels, Hojjat Salehinejad","doi":"10.1109/EMBC53108.2024.10781606","DOIUrl":"10.1109/EMBC53108.2024.10781606","url":null,"abstract":"<p><p>Increased awareness of the opioid epidemic has resulted in the need to significantly reduce the number of opioids prescribed after surgery. However, up to one in five patients require a refill after discharge. Accurate identification of patients at risk of needing a refill after surgery is critically important, as it has the potential to improve pain control and patient experience while avoiding overprescription of opioids after surgery. In this paper, two graph representation learning methods are proposed for predicting opioid refills in postoperative patients. The first approach represents patients as nodes in a graph and performs node classification. The second approach is based on graph classification where each patient is represented as a graph. Performance results on a real-world retrospective cohort of postoperative patients show that a node classification approach with graph sample and aggregation (GraphSAGE) achieves the best performance in prediction of opioid refill.</p>","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":"143559588","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}

{"title":"RF-induced Heating for Partially-In and Partially-Out Bipolar Parallel Medical Electrodes.","authors":"Md Zahidul Islam, Mir Khadiza Akter, Qingyan Wang, Ran Guo, Jianfeng Zheng, Ji Chen","doi":"10.1109/EMBC53108.2024.10782861","DOIUrl":"10.1109/EMBC53108.2024.10782861","url":null,"abstract":"<p><p>RF-induced heating is evaluated for unipolar and bipolar Partially-In and Partially-Out (PIPO) medical electrodes at 1.5T MRI. Numerical simulations were performed by modeling simplified unipolar and bipolar electrodes to understand the RF heating mechanism. Then, experimental studies inside the ASTM phantom were performed using a 60 cm long commercial unipolar and bipolar PIPO cardiac pacing electrodes. In addition, transfer function models were developed, scaled, and validated for 60 cm pacing electrodes, and in-vivo heating was estimated for 30-minute RF exposure using the standard medium. The results show that the RF heating for the bipolar PIPO medical electrode is lower than the unipolar PIPO electrode due to coupling between the parallel leads. However, this study uses limited clinical trajectories for the external pacing application; heating could differ for other possible trajectories, devices, or applications.</p>","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":"143559756","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}

{"title":"Non-invasive stroke diagnosis using speech data from dysarthria patients.","authors":"Sae Byeol Mun, Young Jae Kim, Kwang Gi Kim","doi":"10.1109/EMBC53108.2024.10781716","DOIUrl":"10.1109/EMBC53108.2024.10781716","url":null,"abstract":"<p><p>Acute Ischemic Stroke (AIS) is a major cause of disability and can lead to death in severe cases. A common symptom of AIS, dysarthria, significantly impacts the quality of life of patients. In this study, we developed a deep learning model using dysarthria data for cost-effective and non-invasive brain stroke diagnosis. We utilized models such as ResNet50, InceptionV4, ResNeXt50, SEResNeXt18, and AttResNet50 to effectively extract and classify speech features indicative of stroke symptoms. These models demonstrated high performance, with Sensitivity, Specificity, Precision, Accuracy, and F1-score values reaching 96.77%, 96.08%, 92.82%, 95.52%, and 93.82%, respectively. Our approach offers a non-invasive, cost-effective alternative for early stroke detection, with potential for further accuracy improvements through additional research. This method promises rapid, economical early diagnosis, which could positively impact long-term treatment and healthcare options.</p>","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":"143559804","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}