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":"Alignment-Guided Forward-Distortion Model for Deep Unsupervised Correction of Susceptibility Artifacts in EPI.","authors":"Muhammed Hasan Kayapinar, Abdallah Zaid Alkilani, M Okan Irfanoglu, Emine Ulku Saritas","doi":"10.1109/EMBC58623.2025.11251667","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251667","url":null,"abstract":"<p><p>In magnetic resonance imaging (MRI), susceptibility-induced distortions pose a significant challenge for images acquired using echo planar imaging (EPI). Classical methods use two EPI images acquired in reverse phaseencoding (PE) directions to correct susceptibility artifacts. However, these methods suffer from long computation times, making them impractical for clinical usage. Recently, deep learning-based approaches have been proposed to enable a leap in computation efficiency for EPI susceptibility artifact correction. A vital consideration in reverse-PE-based correction is the need to take into account any potential subject motion between reversed-PE acquisitions. In this work, we propose an alignment-guided forward distortion network (agFD-Net) that accounts for subject motion during correction of susceptibility artifacts. Similar to its predecessor FD-Net, agFD-Net is trained in a physics-driven unsupervised fashion to estimate a single corrected image and a displacement field. In agFD-Net, a new pre-trained alignment network called AlignNet is plugged into the network architecture to facilitate motion correction. The results on experimental NIH dataset featuring realistic levels of motion demonstrate that agFD-Net provides rapid and high-fidelity artifact correction, while successfully accounting for subject motion.Clinical Relevance-EPI is the most commonly used sequence for diffusion MRI and functional MRI. While susceptibility artifacts in EPI require correction before any downstream evaluation, the long computation time of classical correction methods make them impractical for use in clinical settings. The proposed agFD-Net provides more than two orders of magnitude speed up in computational efficiency, making it a highly promising approach for use in clinical settings.</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":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671253","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":"Effectiveness of Extracorporeal Shock Wave Therapy for Rapid Relief of Exercise-induced Muscle Fatigue.","authors":"Yunjie Feng, Liansheng Xu, Fengji Li, Fei Shen, Fan Fan, Qiong Wu, Li Wang, Haijun Niu","doi":"10.1109/EMBC58623.2025.11253826","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253826","url":null,"abstract":"<p><p>Extracorporeal shock wave therapy (ESWT) is a commonly used physical therapy method in clinical and rehabilitation fields. This paper assesses the effect of ESWT in the rapid relief of exercise-induced muscle fatigue, based on fatigue protocol and ESWT intervention experiments, combined with subjective and objective fatigue evaluation methods. After 22 subjects experienced exercise-induced biceps brachii fatigue, the experimental group received a single 250-second ESWT intervention, while the control group underwent placebo treatment. During the experiment, the Borg Rating of Perceived Exertion (RPE) scores, maximum voluntary contraction (MVC), and electromyographic (EMG) data were recorded, and the values of EMG time-domain and frequency-domain parameters were calculated. After intervention, the MVC values of the experimental group significantly recovered, and the EMG parameters returned to baseline levels, all of which showed significant differences compared to the control group. The RPE scores also significantly recovered, but the recovery effect was not significantly better than that of the control group. A single session of ESWT intervention can effectively improve exercise-induced muscle fatigue, characterized by the recovery of muscle strength and the improvement of electrophysiological status.</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":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671254","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":"Automated Facial Expression Analysis: A New Framework for Comparing Facial Muscle Activity Across Individuals.","authors":"Hila Man, Paul F Funk, Chen Bar-Haim, Bara Levit, Orlando Guntinas-Lichius, Yael Hanein","doi":"10.1109/EMBC58623.2025.11254483","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254483","url":null,"abstract":"<p><p>Objective and precise measurement of facial muscle activity is crucial for understanding emotional expressions across diverse stimuli. However, traditional methods often require controlled lab environments, limiting real-world applicability. In this study, we introduce an automated framework that integrates wireless facial surface electromyography (sEMG) with a robust facial muscle Atlas for comprehensive facial expression analysis. This approach enables cross-subject and inter-subject measurements, facilitating applications in psychology, affective computing, and clinical research. We validate our framework by analyzing facial muscle activity in response to olfactory, visual, and auditory emotional stimuli, demonstrating its efficacy in capturing nuanced expression dynamics.Clinical relevance- This work advances emotion research by providing a scalable and objective tool for facial expression analysis, with potential applications in psychology and medical diagnostics, particularly in conditions where facial expressions play a crucial role.</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":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671228","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":"Changes in Signal Morphology of 3D Printed Dry electrodes for Enhanced ECG Signal Quality in Dynamic Conditions.","authors":"Lohitaa J, Krushna Devkar, Mythili Asaithambi, Nagarajan Ganapathy","doi":"10.1109/EMBC58623.2025.11252823","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252823","url":null,"abstract":"<p><p>Traditional gel-based electrodes are widely used for bioelectric signal acquisition, but they come with drawbacks such as skin irritation, signal degradation over time, and the need for frequent replacements, making them less ideal for long-term health monitoring scenarios. This study explores the development of 3D-printed dry electrodes as a gel-free, reusable alternative for ECG monitoring. Gel-less electrodes of six distinct electrode geometries namely flat, dome, flat needle, curved needle, half pointed, and Pointed were fabricated using fused deposition modeling (FDM) with conductive polylactic acid (PLA) to evaluate the impact of shape on signal quality. ECG data collected in both resting and exercise conditions was evaluated by measuring the electrodes' electrical conductivity and analyzing key parameters, including kurtosis, skewness, signal-to-noise ratio (SNR), and standard deviation of Normal-to-Normal Intervals (SDNN), to compare their performance against conventional gel-based electrodes. The results demonstrate that 3D-printed dry electrodes can provide high-quality signals comparable to conventional electrodes in wild scenarios. Among the tested designs, the flat and FN electrodes exhibited the best performance making them particularly effective under both static and dynamic conditions. Future work will focus on enhancing flexibility, stretchability, and adhesion to improve comfort and long-term usability, making them more suitable for continuous, real-time monitoring in wearable healthcare 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":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671240","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":"Impact of latency jitter correction on offline P300-based classification: a preliminary study for BCI applications in MCS patients.","authors":"V Caracci, A Riccio, M D'Ippolito, V Galiotta, I Quattrociocchi, R Formisano, F Cincotti, J Toppi, D Mattia","doi":"10.1109/EMBC58623.2025.11253369","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253369","url":null,"abstract":"<p><p>Disorders of Consciousness (DoC) are clinical conditions characterized by different levels of arousal and awareness, including coma, Unresponsive Wakefulness Syndrome and Minimally Conscious State (MCS). A Brain-Computer Interface (BCI) employs brain signals to establish a non-muscular outward channel, representing a key frontier in the clinical care of individuals in MCS, with high potential to enhance communication and quality of life. The P300-based BCIs, which use the P300 ERP as a control signal, are the most investigated to emulate communication in MCS. However, a reliable control by MCS patients of these BCIs still remains matter of question. One major challenge could be the across trials variability of P300 characteristics, possibly related to attentional fluctuations in this population. The trial-by-trial instability of the P300 peak latency, known as latency jitter, negatively impacts classification performance, and an approach to mitigating this issue involves template matching algorithms (e.g. the Adaptive Wavelet Filtering, AWF) which detect and realign the P300 latency at the single-trial level. This study investigated the offline classification performance using Stepwise Linear Discriminant Analysis (SWLDA) models trained with progressively larger training sets, to discriminate target from non-target stimuli during an active auditory oddball paradigm. Performance from raw and jitter-corrected data, collected from a control group and a group of patients diagnosed as MCS, were compared. Results highlighted the key role of latency jitter correction in the enhancement of performance and classification speed.Clinical Relevance- The findings suggest that jitter correction could improve real-world applicability of P300-BCI systems for individuals with DoC.</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":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671244","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":"Analyzing the Resonant Behavior of a Single Neuron at the Subthreshold Level.","authors":"David D Mao, Ariel R Yin, Yangfan Deng, Yao Lin, Ker-Jiun Wang, Ramana Vinjamuri","doi":"10.1109/EMBC58623.2025.11253657","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253657","url":null,"abstract":"<p><p>Different brain rhythms are often signatures of different behavioral and perceptual states of the brain. As resonance at neurons has a close connection with brain rhythms, characterizing and analyzing the neuronal resonance promote the understanding of brain rhythms and dynamical organization of the brain. In this paper, we study the resonant behaviors of both the four-dimensional (4D) Hodgkin-Huxley model and a reduced-order (2D) model at the subthreshold level. We analyze the frequency responses of a neuron and characterize resonance through investigating the neuron's transfer functions, frequency response functions, and root locus plots. The 2D model, especially, allows us to visualize the state space, perform phase plane analysis, and derive a closed-form formula for the resonant frequency. These analyses will be generalized to study resonance at the spiking regime and network level in future work.</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":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671206","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 EEG-Based Method for Detecting Flow State Using a Wearable Headband in a Game Environment<sup />.","authors":"Matin Beiramvand, Reijo Koivula, Tarmo Lipping","doi":"10.1109/EMBC58623.2025.11251885","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251885","url":null,"abstract":"<p><p>Flow is a mental state of deep focus and immersion, associated with enhanced productivity, creativity, and well-being. While its benefits are well-documented, research on its neural basis remains emerging. EEG-based systems offer a practical approach to flow detection due to their simplicity and accessibility. Recent advancements in commercial EEG devices provide new opportunities for real-life flow detection, yet their potential remains underexplored. In this study, we analyzed 29 EEG recordings of participants playing a Tetris video game, utilizing data from a consumer-oriented EEG device to develop a low-channel method for detecting the flow state. After denoising the EEG signals, We applied the Discrete Wavelet Transform (DWT) to decompose the signals into sub-bands. From each sub-band, we extracted three entropy-based features: Slope Entropy, Distribution Entropy, and Spectral Entropy. The extracted features were subsequently fed into a Random Forest classifier using two cross-validation strategies: Random Sampling (RS) and Leave-One-Subject-Out (LOSO). The classifier demonstrated high accuracy, achieving a mean accuracy of 93% with Random Sampling validation. Additionally, the LOSO validation strategy yielded an 82% average accuracy across the dataset. These findings suggest that the proposed method is a promising approach for detecting the flow state in real-life 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":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671211","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":"Anatomy-aware Assessment of Critical View of Safety with Spatial Relation Prior.","authors":"Yunfan Li, Haibin Ling, Himanshu Gupta","doi":"10.1109/EMBC58623.2025.11251617","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251617","url":null,"abstract":"<p><p>The Critical View of Safety (CVS) is a set of widely accepted conditions that must be met during laparoscopic cholecystectomy (LC) surgeries, and it has been an important validation method for the prevention of bile duct injuries (BDIs). Recent methods explored using object detection and graph construction to facilitate downstream CVS classification, but given the intrinsic spatial relations between anatomical structures in an LC scene, such prior information has yet to be utilized. In this paper, we propose incorporating prior knowledge about spatial relations among anatomical structures to improve the assessment of CVS. We evaluated our method on the publicly available Endoscapes dataset, and achieved 1 ∼ 7% improvement on the baseline model in individual CVS conditions and over 9% in overall CVS 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":"2025 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671278","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":"Compressed Sensing of Acoustic Cardiopulmonary Signals Using a CNN-based Reconstruction Method.","authors":"Rens Baeyens, Domenico Ragusa, Toon Stas, Kris Ides, Elisa Marenzi, Francesco Leporati, Walter Daems, Jan Steckel","doi":"10.1109/EMBC58623.2025.11253482","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253482","url":null,"abstract":"<p><p>Cardiopulmonary sounds contain a rich reservoir of vital and pathological information critical for clinical diagnosis. This paper presents a novel approach to cardiopulmonary data capturing with compressive sensing and reconstruction using a Convolutional Neural Network (CNN) based on the U-Net architecture. Applying traditional compressive sensing techniques to cardiopulmonary sounds presents several challenges. Cardiopulmonary sounds are inherently complex, with a substantial variation between captures. The traditional algorithms for compressive sensing rely on signal sparsity, whereas finding a sparse representation domain for cardiopulmonary sounds is a difficult task. Instead of finding a sparse domain manually, we propose training a convolutional encoder-decoder neural network for a pseudo-randomly undersampled set of signals without explicitly enforcing the sparsity concept. In this research, a CNN was trained for pseudo-randomly decimated input signals, evaluating a compression ratio of up to 30. The network is trained for respiratory sounds using the SPRSound dataset and for Phonocardiogram (PCG) signals using the CirCor Digiscope PCG dataset. Both these datasets have been evaluated for signal integrity after reconstruction and delivered promising results. The algorithm achieves reconstruction quality similar to that of previous research with a compression ratio three times higher than that of previous research applied to respiratory sounds. Since the principles of compressive sensing are applied in the sampling stage, the data compression requires no computation in the compression stage, and can therefore easily be implemented in low-cost edge devices.Clinical relevance- This work enables efficient compression of cardiopulmonary sounds, maintaining high signal integrity even at three times higher compression ratios than previous methods applied to respiratory sounds. It supports low-power, portable devices for real-time monitoring, improving accessibility for telemedicine and point-of-care diagnostics in respiratory and cardiovascular conditions.</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":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671287","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":"Atrial Fibrillation Detection from Ambulatory ECG with Accelerometry Contextualisation: A Semi-Supervised Learning Approach.","authors":"Alex E Voinas, Devender Kumar, Jan Smeddinck, Andreas Stochholm, Sadasivan Puthusserypady","doi":"10.1109/EMBC58623.2025.11251627","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251627","url":null,"abstract":"<p><p>Atrial fibrillation (AF) is a common cardiac arrhythmia causing severe complications if left untreated. Due to its sporadic nature, early detection often requires longitudinal ambulatory electrocardiogram (ECG) screening. Recently, deep learning (DL) has gained prominence in analysing long-term ECG and automating AF detection. However, like any medical classification problem, obtaining diverse labelled ECG data for DL model training is expensive and time-consuming. This paper proposes a semi-supervised learning (SSL) based AF detection model employing a variational auto-encoder (VAE). It leverages varying amounts of labelled and unlabelled ECG data to optimise the AF detection performance on ambulatory ECG. As ambulatory contexts under free-living conditions influence ECG recordings, we incorporate context via accelerometry data and experiment with its influence on model performance. The proposed SSL model was trained on ECG data from 72,003 unique patients and can classify between sinus rhythms, AF, and other arrhythmias. Experimental results on unseen test dataset and the publicly available CACHET-CADB dataset clearly demonstrate the model's generalisability, achieving an accuracy of over 91% with just 20% of the training set being labelled. With extensive experiments, our study exhibits the ability of SSL to improve AF detection from ambulatory ECG using small amounts of labelled data.</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":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671303","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}