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3页

High-Resolution Time-Frequency Analysis of EEG Signals for Affective Computing. 面向情感计算的脑电信号高分辨率时频分析。

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/EMBC53108.2024.10782482

Yedukondala Rao Veeranki, Hugo F Posada-Quintero

{"title":"High-Resolution Time-Frequency Analysis of EEG Signals for Affective Computing.","authors":"Yedukondala Rao Veeranki, Hugo F Posada-Quintero","doi":"10.1109/EMBC53108.2024.10782482","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782482","url":null,"abstract":"Affective computing is a critical aspect of human-computer interaction. Electroencephalographic (EEG) signals, which reflect electrical brain activity, are widely used for the understanding of human emotional states. However, these signals are nonlinear and nonstationary, making traditional analysis methods insufficient. To address these challenges, recent studies have focused on time-frequency analysis. In this paper, we propose a variable frequency complex demodulation (VFCDM) approach to obtain high-resolution time-frequency spectra (TFS) from EEG signals. First, we compute the TFS using the time-varying optimal parameter search technique to capture the spectral information. Then we generate VFCDM sub-bands and extract statistical features from each of the sub-bands. These features are then used with the Random Forest algorithm to classify arousal and valence dimensions. Our results demonstrate the robustness of this approach and its ability to accurately discriminate complex affective dimensions. The δ-VFCDM and γ-VFCDM bands produced the highest F1 scores of 71.80% for Arousal and 69.55% for Valence differentiation. This work significantly advances EEG-based affective computing and opens avenues for more emotionally attuned human-computer interaction systems.","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":"143558855","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 Fast Patch-Based Hankel Low-Rank Method for Magnetic Resonance Spectroscopy Reconstruction. 基于快速贴片的Hankel低秩磁共振谱重建方法。

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/EMBC53108.2024.10782347

Hengfa Lu, Xinlin Zhang

引用次数: 0

A federated stroke segmentation to impact limited data institutions. 影响有限数据机构的联合卒中分割。

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/EMBC53108.2024.10781772

Edgar Rangel, Santiago Gomez, Daniel Mantilla, Paul Camacho, Fabio Martinez

{"title":"A federated stroke segmentation to impact limited data institutions.","authors":"Edgar Rangel, Santiago Gomez, Daniel Mantilla, Paul Camacho, Fabio Martinez","doi":"10.1109/EMBC53108.2024.10781772","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781772","url":null,"abstract":"Stroke, predominantly caused by blood vessel occlusion, is the second leading cause of death worldwide. DWI sequences facilitate characterization of brain-affected tissue, enabling lesion volume estimation, guiding treatment protocols, and aiding in prognosis approximation. However, radiological interpretations rely on neuroradiologist expertise, introducing subjectivity. Currently, computational solutions have allowed to support lesion characterization, but such efforts are dedicated to learn patterns from only one institution, lacking the variability to generalize geometrical lesion shape models. Moreover, some institutions lack training samples in annotated batches, which makes it difficult to achieve personalized solutions. This work introduces the first federated approach to stroke segmentation, leveraging data across institutions to impact institutions without data requirements. Models were trained on diverse institutional data and combined to obtain a robust solution for those without annotated datasets. Also, from such federated scheme was possible to measure the generalization capability of state-of-the-art architectures, evidencing new challenges in stroke care support.Clinical relevance- The validation of federated collaborative solutions to support stroke segmentations to transfer in clinical scenarios.","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":"143558912","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 signal processing tool for extracting features from arterial blood pressure and photoplethysmography waveforms. 从动脉血压和光容积脉搏波波形中提取特征的信号处理工具。

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/EMBC53108.2024.10782973

R Pal, A Rudas, S Kim, J N Chiang, M Cannesson

{"title":"A signal processing tool for extracting features from arterial blood pressure and photoplethysmography waveforms.","authors":"R Pal, A Rudas, S Kim, J N Chiang, M Cannesson","doi":"10.1109/EMBC53108.2024.10782973","DOIUrl":"10.1109/EMBC53108.2024.10782973","url":null,"abstract":"Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms contain valuable clinical information and play a crucial role in cardiovascular health monitoring, medical research, and managing medical conditions. The features extracted from PPG waveforms have various clinical applications ranging from blood pressure monitoring to nociception monitoring, while features from ABP waveforms can be used to calculate cardiac output and predict hypertension or hypotension. In recent years, many machine learning models have been proposed to utilize both PPG and ABP waveform features for these healthcare applications. However, the lack of standardized tools for extracting features from these waveforms could potentially affect their clinical effectiveness. In this paper, we propose an automatic signal processing tool for extracting features from ABP and PPG waveforms. Additionally, we generated a PPG feature library from a large perioperative dataset comprising 17,327 patients using the proposed tool. This PPG feature library can be used to explore the potential of these extracted features to develop machine learning models for non-invasive blood pressure estimation.","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":"143558918","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 Longitudinal Study on Fingerprint Recognition in Infants, Toddlers, and Children. 婴幼儿和儿童指纹识别的纵向研究。

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/EMBC53108.2024.10781797

Mst Rumana A Sumi, Masudul H Imtiaz, Stephanie Schuckers

{"title":"A Longitudinal Study on Fingerprint Recognition in Infants, Toddlers, and Children.","authors":"Mst Rumana A Sumi, Masudul H Imtiaz, Stephanie Schuckers","doi":"10.1109/EMBC53108.2024.10781797","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781797","url":null,"abstract":"Millions of children in developing countries face preventable deaths due to inadequate vaccination and malnutrition, in part due to insufficient monitoring and the absence of official identification. A reliable fingerprint recognition system can be a practical solution to address this issue. However, the scarcity of longitudinal fingerprint datasets for young children leads to unresolved questions regarding the earliest age for fingerprint biometric use, the frequency of enrollment required for reliable recognition, and the methods to accommodate age-related changes. A few recent studies introduced high-resolution fingerprint scanners and showed promising recognition performance for young children. However, these studies were conducted on a small dataset over a shorter period with limited diversity; further evaluation of their finding is essential. This study assessed the effectiveness of a high-resolution contactless scanner in a controlled, diverse longitudinal dataset of children (0-15 years). Our results indicate that infants can be enrolled at five days old and reliably recognized after two months with a TAR= 100% @ FAR = 0.1%, and children aged 4-15 years can be recognized after one year with a TAR= 98.72% @ FAR = 0.1%.","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":"143558930","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 Multi-Contrast Translation-Based Registration Approach for Distortion Correction in DTI. 基于多对比翻译的DTI图像失真校正配准方法。

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/EMBC53108.2024.10781931

Ya Cui, Siyu Yuan, Zhenkui Wang, Li Tong, Jie Luo

{"title":"A Multi-Contrast Translation-Based Registration Approach for Distortion Correction in DTI.","authors":"Ya Cui, Siyu Yuan, Zhenkui Wang, Li Tong, Jie Luo","doi":"10.1109/EMBC53108.2024.10781931","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10781931","url":null,"abstract":"Correcting eddy currents and motion artifacts is crucial in Diffusion Tensor Imaging (DTI) preprocessing, traditionally managed through affine registration to an undistorted reference. However, the contrast variation across diffusion-weighted images complicates direct registration. To surmount this challenge, our study introduces a translation-based registration approach, utilizing 312 DTI datasets from the Human Connectome Project (HCP), including both b=0 and b=2000 volumes. We employed an advanced 3D Self-Attention Conditional Generative Adversarial Network (SC-GAN) for the synthesis of imaging data. This method allowed for the generation of synthesized b=2000 volumes, enhancing the distortion-correction process by facilitating efficient registration of distorted images. The results showed the translation network's effectiveness in synthesizing b=2000 volumes from real data, with these volumes serving as stable registration targets, particularly in limited directional data scenarios. The approach also effectively corrected eddy current and motion artifacts, aligning FA and FOD maps with gold standard results from FSL's eddy method, confirming the translation-based registration's efficacy in addressing cross-contrast registration challenges in DTI preprocessing.","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":"143558936","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 Multimodal Myanmar Emotion Dataset for Emotion Recognition. 用于情绪识别的多模态缅甸情绪数据集。

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/EMBC53108.2024.10782660

Khin Pa Pa Aung, Hao-Long Yin, Tian-Fang Ma, Wei-Long Zheng, Bao-Liang Lu

{"title":"A Multimodal Myanmar Emotion Dataset for Emotion Recognition.","authors":"Khin Pa Pa Aung, Hao-Long Yin, Tian-Fang Ma, Wei-Long Zheng, Bao-Liang Lu","doi":"10.1109/EMBC53108.2024.10782660","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782660","url":null,"abstract":"Effective emotion recognition is vital for human interaction and has an impact on several fields such as psychology, social sciences, human-computer interaction, and emotional artificial intelligence. This study centers on the innovative contribution of a novel Myanmar emotion dataset to enhance emotion recognition technology in diverse cultural contexts. Our unique dataset is derived from a carefully designed emotion elicitation paradigm, using 15 video clips per session for three emotions (positive, neutral, and negative), with five clips per emotion. We collected electroencephalogram (EEG) signals and eye-tracking data from 20 subjects, and each subject took three sessions spaced over several days. Notably, all video clips used in experiments have been well rated by Myanmar citizens through the Self-Assessment Manikin scale. We validated the proposed dataset's uniqueness using three baseline unimodal classification methods, alongside two traditional multimodal approaches and a deep multimodal approach (DCCA-AM) under subject-dependent and subject-independent settings. Unimodal classification achieved accuracies ranging from 62.57% to 77.05%, while multimodal fusion techniques achieved accuracies ranging from 75.43% to 87.91%. These results underscore the effectiveness of the models, and highlighting the value of our unique dataset for cross-cultural applications.","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":"143558938","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 Novel Machine-Learning-Based Noise Detection Method for Photoplethysmography Signals. 一种新的基于机器学习的光容积脉搏波信号噪声检测方法。

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/EMBC53108.2024.10782126

Soheil Khooyooz, Anice Jahanjoo, Amin Aminifar, Nima TaheriNejad

引用次数: 0

Beyond the Game: Multimodal Emotion Recognition Before, During, and After Gameplay. 超越游戏：游戏玩法之前、期间和之后的多模态情感识别

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/EMBC53108.2024.10782547

Efstratia Ganiti-Roumeliotou, Ioannis Ziogas, Sofia B Dias, Ghada Alhussein, Herbert F Jelinek, Leontios J Hadjileontiadis

{"title":"Beyond the Game: Multimodal Emotion Recognition Before, During, and After Gameplay.","authors":"Efstratia Ganiti-Roumeliotou, Ioannis Ziogas, Sofia B Dias, Ghada Alhussein, Herbert F Jelinek, Leontios J Hadjileontiadis","doi":"10.1109/EMBC53108.2024.10782547","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782547","url":null,"abstract":"In the era of Human-Computer Interaction (HCI), understanding emotional responses through multimodal signals during interactive experiences, such as serious games (SG), is of high importance. In this work, we explore emotion recognition (ER) by analyzing multimodal data from the 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE-2) dataset, including data from 76 participants engaged in dynamic gameplay and pre-post audiovisual stimulations. Utilizing features derived from electrocardiogram (ECG), electrodermal activity (EDA), accelerometer, gyroscope, game logs (GL), affect dynamics and personality traits (PT) fed in different machine learning models, our study focuses on ER, achieving state-of-the-art performance across different experimental scenarios (accuracy: 0.967 for Negative Affect in Optimal Game using Support Vector Machines). This highlights the importance of emotional states as indicators for personalized HCI. Our approach offers valuable insights to understanding the interplay between multimodal physiological signals, GL, user's emotional states and PT, which could add to the design of adaptive, affect-sensitive SG. Distinct patterns in the data are revealed, particularly emphasizing the role of ECG-Derived Respiration features and the impact of past affectivity to current emotional state.Clinical relevance-By introducing innovative perspectives in affect-sensitive SG design, leveraging the analysis of multimodal signals, we foresee objective digital biomarkers that hold promise to broaden the clinical understanding of patients' emotional behavior during SG-based interventions.","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":"143558965","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. 一种植入式睫状肌LFP记录与传输系统。

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