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

Local Activation Identification in Persistent Atrial Fibrillation Intracardiac EGM Signals for Automatic Spatio-temporal Dispersion Pattern Recognition. 持续性房颤心内EGM信号的局部激活识别用于自动时空离散模式识别。

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/EMBC53108.2024.10782300

Sara Frusone, Rafael Costa De Almeida, Fabien Squara, Vicente Zarzoso

{"title":"Local Activation Identification in Persistent Atrial Fibrillation Intracardiac EGM Signals for Automatic Spatio-temporal Dispersion Pattern Recognition.","authors":"Sara Frusone, Rafael Costa De Almeida, Fabien Squara, Vicente Zarzoso","doi":"10.1109/EMBC53108.2024.10782300","DOIUrl":"10.1109/EMBC53108.2024.10782300","url":null,"abstract":"Atrial fibrillation (AF) is a common cardiac condition that predominantly affects the elderly population, presenting a significant risk factor for strokes and thus raising concerns in public health. Catheter ablation (CA) stands out as the most effective long-term treatment for persistent AF. A recently proposed novel CA approach is based on spatio-temporal dispersion (STD). This technique targets the STD patterns associated with active zones responsible for sustaining the arrhythmia. In this work we want to solve the peak detection problem, since it is a fundamental step for the automatic classification of STD patterns from multipolar electrograms (EGM). Instead of using machine learning models which lacks explainability, we want to understand the classification process performed at the block by interventional cardiologists in real time. The scenario is very challenging because the STD classification relies on visual peak detection to identify local activations, which are used to measure if STD does occur or not. We present our peak detector comparing it with eight different techniques from the state of the art. To extract peaks from real intracardiac EGM signals is difficult, most classical signal processing methods fail. We evaluate a total of nine techniques on the challenging scenario of real STD data. We analyze if the peaks are correctly identified, being part of the mathematical pipeline. Results show that identifying the peaks is a fundamental aspect to built the presented mathematical pipeline to overcome the STD classification problem, improving the classification accuracy with respect to previous works.","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":"143559399","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

Ex vivo studies of efficacy of DeepFocus: a technique for minimally-invasive deep-brain stimulation. 深度聚焦的体外研究：一种微创深部脑刺激技术。

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/EMBC53108.2024.10781751

Yuhyun Lee, Vishal Jain, Maysamreza Chamanzar, Pulkit Grover, Mats Forssell

{"title":"Ex vivo studies of efficacy of DeepFocus: a technique for minimally-invasive deep-brain stimulation.","authors":"Yuhyun Lee, Vishal Jain, Maysamreza Chamanzar, Pulkit Grover, Mats Forssell","doi":"10.1109/EMBC53108.2024.10781751","DOIUrl":"10.1109/EMBC53108.2024.10781751","url":null,"abstract":"Invasive deep-brain stimulation is increasingly being investigated as a treatment for neural disorders. A non-invasive alternative for deep-brain neuromodulation would likely broaden the range of application. However, existing techniques, such as transcranial electrical or magnetic stimulation (TES, TMS), are limited in their depth of stimulation. In this work, we propose DeepFocus, a new minimally invasive approach for stimulation of the deep brain by inserting electrodes in nasal cavities in conjunction with conventional scalp electrodes. As an initial step, an ex vivo model was designed to quantify the current efficiency of the proposed electrode placement in eliciting neural responses. A simplified geometric configuration was employed, where two linear electrode arrays arranged perpendicularly were used to elicit local field potentials (LFP) in mouse brain slices. Through a combination of finite element simulations to model the electric fields, and LFP measurements, we observed that electrode-patterns that use both arrays (modeling transnasal and scalp electrodes) generated higher electric fields and required less current to evoke responses compared to those that use only a single array (modeling scalp-only or transnasal-only). The benefits of two-array stimulation increased as the distance between the electrodes and the brain slice was increased. In addition, we observed that the relative orientation of the electric field compared to the cortical columns affected the neural responses.","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":"143559245","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

Comparative Analysis of Photoplethysmogram (PPG) Waveform Characteristics Across Various Body Sites Under Normal and Apneic Conditions. 正常和呼吸暂停状态下不同身体部位的光容积脉搏图（PPG）波形特征的比较分析。

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/EMBC53108.2024.10781801

Joe Rahme, Sahera Saleh, Tamara Al-Sadek, Jason Amatoury, Massoud Khraiche

引用次数: 0

Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection. 利用3D激光雷达传感器增强城市安全和公共卫生：行人监测和异常活动检测。

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/EMBC53108.2024.10782331

Nawfal Guefrachi, Jian Shi, Hakim Ghazzai, Ahmad Alsharoa

{"title":"Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection.","authors":"Nawfal Guefrachi, Jian Shi, Hakim Ghazzai, Ahmad Alsharoa","doi":"10.1109/EMBC53108.2024.10782331","DOIUrl":"10.1109/EMBC53108.2024.10782331","url":null,"abstract":"The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being. This paper proposes a novel framework that utilizes these technologies for enhanced 3D object detection and activity classification in urban traffic scenarios. By employing elevated LiDAR, we obtain detailed 3D point cloud data, enabling precise pedestrian activity monitoring. To overcome urban data scarcity, we create a specialized dataset through simulated traffic environments in Blender, facilitating targeted model training. Our approach uses a modified Point Voxel Region-Based Convolutional Neural Network (PV-RCNN) for robust 3D detection and PointNet for classifying pedestrian activities, significantly benefiting urban traffic management and public health by offering insights into pedestrian behavior and promoting safer urban environments. Our dual-model approach not only enhances urban traffic management but also contributes significantly to public health by providing insights into pedestrian behavior and promoting safer urban environment.","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":"143559665","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

Development of a soft-type glove capable of customizing finger rehabilitation exercises considering differences in physique^. 一种能够根据体质差异定制手指康复练习的软型手套的开发。

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/EMBC53108.2024.10782734

Ritsuki Nishizawa, Tetsuya Hasegawa, Shouhei Shirafuji, Jun Ota, Arito Yozu

{"title":"Development of a soft-type glove capable of customizing finger rehabilitation exercises considering differences in physique.","authors":"Ritsuki Nishizawa, Tetsuya Hasegawa, Shouhei Shirafuji, Jun Ota, Arito Yozu","doi":"10.1109/EMBC53108.2024.10782734","DOIUrl":"10.1109/EMBC53108.2024.10782734","url":null,"abstract":"When designing a hand rehabilitation device, it is important to account for the differences in physique among patients and tailor the rehabilitation exercises to each patient's needs. This paper presents the prototype of a soft cable-driven glove capable of easily adjusting the glove to fit the individual physique of the patient and customizing finger rehabilitation exercises. Considering the length of the phalanges and the thickness of the finger, the position of fixed parts on the index finger can be easily adjusted. In order to enable various index finger rehabilitation appropriate for the patient, four cable-based cable routing for the index finger was adopted. The kinematic model of the glove was experimentally verified and utilized to plan the rehabilitation exercises. Based on this kinematic model, four representative finger rehabilitation exercises were operated with the developed glove on four different sizes of artificial fingers. The adaptability and adjustability of the glove to four artificial fingers were proved by demonstrating the possibility of assisting with various rehabilitation exercises while driving the three joints of the index finger independently.","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":"143559316","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 Improvised Approach Using YOLOv3 Architecture for Digital Panoramic Teeth Recognition and Classification. 一种基于YOLOv3架构的数字全景牙齿识别与分类简易方法。

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":"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. 超声模体定量校准研究：深度对衰减和后向散射特性的影响。

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

Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy. 生成式人工智能能学习生理波形形态吗？缺血性心肌病心内信号去噪的研究。

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":"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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12061072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data. 利用原始脑电图数据评估基于深度学习的重度抑郁症诊断增强方法。

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/EMBC53108.2024.10782103

Charles A Ellis, Robyn L Miller, Vince D Calhoun

{"title":"Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.","authors":"Charles A Ellis, Robyn L Miller, Vince D Calhoun","doi":"10.1109/EMBC53108.2024.10782103","DOIUrl":"10.1109/EMBC53108.2024.10782103","url":null,"abstract":"While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. Data augmentation (DA) could address this limitation, but the utility of EEG DA methods remains relatively underexplored in neuropsychiatric disorder diagnosis. In this study, we train a model for major depressive disorder diagnosis. We then evaluate the utility of 6 EEG DA approaches. Importantly, to remove the bias that could be introduced by comparing performance for models trained on larger augmented training sets to models trained on smaller baseline sets, we also introduce a new baseline trained on duplicate training data. We lastly examine the effects of the DA approaches upon representations learned by the model with a pair of explainability analyses. We find that while most approaches boost model performance, they do not improve model performance beyond that of simply using a duplicate training set without DA. The exception to this is channel dropout augmentation, which does improve model performance. These findings suggest the importance of comparing EEG DA methods to a baseline with a duplicate training set of equal size to the augmented training set. We also found that some DA methods increased model robustness to frequency (Fourier transform surrogates) and channel (channel dropout) perturbation. While our findings on EEG DA efficacy are restricted to our dataset and model, we hope that future studies on deep learning for small EEG datasets and on new EEG DA methods will find our findings helpful.","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":"143559536","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

Toward EEG-Based Objective Assessment of Emotion Intensity. 基于脑电图的情绪强度客观评估。

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/EMBC53108.2024.10781662

Pin-Han Ho, Yong-Sheng Chen, Chun-Shu Wei

引用次数: 0