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5页

Can Camera-PPG Imaging be Used to Measure Perfusion Index?

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/EMBC53108.2024.10781667

Zhiyuan Xu, Yukai Huang, Ningbo Zhao, Jia Huang, Hongzhou Lu, Wenjin Wang

引用次数: 0

Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy.

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":"https://doi.org/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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559206","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

Classifying Driver Distraction with Textile Electrocardiograms.

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/EMBC53108.2024.10782613

Kaveti Pavan, Vishal Singh Roha, Tomohiko Igasaki, P A Karthick, Digvijay S Pawar, Nagarajan Ganapathy

{"title":"Classifying Driver Distraction with Textile Electrocardiograms.","authors":"Kaveti Pavan, Vishal Singh Roha, Tomohiko Igasaki, P A Karthick, Digvijay S Pawar, Nagarajan Ganapathy","doi":"10.1109/EMBC53108.2024.10782613","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782613","url":null,"abstract":"Textile sensor-based vital sign assessment plays an important role in continuous monitoring due to its unobtrusive and non-invasiveness. Textile electrocardiography (ECG) sensors allow mental wellbeing assessments in drivers during driving. In this study, we assess the effectiveness of a single-lead ECG obtained from a non-medical-grade ECG shirt for detecting driver distraction due to induced stress. Using ECG shirts, a single-lead ECG (256Hz, 12 bits) is acquired from N=10 healthy volunteers having driving licenses in three distinct driving situations (Baseline, Texting, Calling) in a controlled environment. ECG data is manually checked, and segmented into short durations (10, 30, 60 seconds). These segments are applied to a customized convolution neural network (ccNN). The proposed approach is able to classify the driver's distraction with ccNN yielding a weighted F-Score of 0.65 and an average accuracy of 67.12% on the validation set. Leave-One-Subject-Out Cross-Validation results showed weighted F-Scores ranging from 0.53 to 0.75. Thus, a single-lead, wearable textile ECG provides informative insights into a driver's mental wellbeing.","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":"143559231","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

Clean Synthesis of ZnO-Au Nanoconjugate Inks for Bandgap Tuning Applications.

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/EMBC53108.2024.10782882

Harikrishnan Muraleedharan Jalajamony, Soumadeep De, Renny Edwin Fernandez

引用次数: 0

Deep Learning Method for Estimating Germ-layer Regions of Early Differentiated Human Induced Pluripotent Stem Cells on Micropattern Using Bright-field Microscopy Image.

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/EMBC53108.2024.10782655

Slo-Li Chu, Hideo Yokota, Pai-Ting Wang, Kuniya Abe, Yohei Hayashi, Dooseon Cho, Ming-Dar Tsai

{"title":"Deep Learning Method for Estimating Germ-layer Regions of Early Differentiated Human Induced Pluripotent Stem Cells on Micropattern Using Bright-field Microscopy Image.","authors":"Slo-Li Chu, Hideo Yokota, Pai-Ting Wang, Kuniya Abe, Yohei Hayashi, Dooseon Cho, Ming-Dar Tsai","doi":"10.1109/EMBC53108.2024.10782655","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782655","url":null,"abstract":"Live cell staining is expensive and may bring potential safety issues in downstream clinical applications, bright-field images rather than staining images should be more suitable to reveal time-series changes of differentiating hiPSCs (human induced pluripotent stem cells) and three-germ layers differentiated from the hiPSCs. This study proposed a deep learning method for estimating immunofluorescence regions on a bright-field microscopy images. The networks trained by multiple types of fluorescence images can estimate the types of fluorescence images from a bright-field image. The estimated pseudo Hoechst image is used to segment hiPSCs, and the others classify the segmented hiPSCs as respective germ-layer cells. The experimental results show over 75% correct rates for the segmentation and classification were achieved, indicating the proposed method can be useful tool in evaluating pluripotency of hiPSC and delineating the germ layer formation process without cell staining.","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":"143559243","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.

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":"https://doi.org/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

Non-invasive stroke diagnosis using speech data from dysarthria patients.

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/EMBC53108.2024.10781716

Sae Byeol Mun, Young Jae Kim, Kwang Gi Kim

引用次数: 0

Reproduction of central-brachial-radial arterial blood pressure wave propagation using a cardiovascular hardware simulator.

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/EMBC53108.2024.10782911

Jae-Hak Jeong, Bomi Lee, Junki Hong, Changhee Min, Adelle Ria Persad, Yong-Hwa Park

{"title":"Reproduction of central-brachial-radial arterial blood pressure wave propagation using a cardiovascular hardware simulator.","authors":"Jae-Hak Jeong, Bomi Lee, Junki Hong, Changhee Min, Adelle Ria Persad, Yong-Hwa Park","doi":"10.1109/EMBC53108.2024.10782911","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782911","url":null,"abstract":"This study reproduced changes according to the central-brachial-radial blood pressure wave propagation using a cardiovascular hardware simulator. Blood pressure is a key indicator of cardiovascular health, and its importance has recently emerged, and research into the correlation between the two is in progress. This requires a large amount of clinical data, but the amount and distribution are limited. The hardware simulator in this study mimics the structure and properties of the human cardiovascular system. This reproduces the pulse wave velocity and the generation of a blood pressure wave. The reproduced central-brachial-radial blood pressure waves are similar to those of humans in magnitude, waveform, and changes due to propagation. Blood pressure waves propagate from the central aorta to the radial artery, showing waveform changes due to systolic amplification and reduced overlap area. Reproducing these blood pressure waveforms can compensate for the lack of quantity and quality in clinical data. In the future, it can be expanded to a testbed for health sensors and research on the origin of bio-signals through the addition of upper arm and wrist phantoms.","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":"143559810","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

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality.

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/EMBC53108.2024.10782633

Mahdi Momeni, Adrian Radomski, Ulkuhan Guler, Daniel Teichmann

{"title":"Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality.","authors":"Mahdi Momeni, Adrian Radomski, Ulkuhan Guler, Daniel Teichmann","doi":"10.1109/EMBC53108.2024.10782633","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782633","url":null,"abstract":"This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness.","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":"143559814","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

Residual Neural Networks for the Prediction of the Regularization Parameters in PET Reconstruction.

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/EMBC53108.2024.10782195

Younes Moussaoui, Diana Mateus, Said Moussaoui, Thomas Carlier, Simon Stute

{"title":"Residual Neural Networks for the Prediction of the Regularization Parameters in PET Reconstruction.","authors":"Younes Moussaoui, Diana Mateus, Said Moussaoui, Thomas Carlier, Simon Stute","doi":"10.1109/EMBC53108.2024.10782195","DOIUrl":"https://doi.org/10.1109/EMBC53108.2024.10782195","url":null,"abstract":"Positron Emission Tomography (PET) is a medical imaging modality relying on numerical methods that integrate the statistical properties of the measurements and prior assumptions about the images. In order to maximize the computed image quality, PET reconstruction algorithms require the setting of hyperparameters that balance data fidelity with regularization. However, their optimal tuning depends on the statistical properties of the raw data and on the clinical objectives. To address this issue, we propose a supervised deep learning strategy based on a residual neural network that takes the raw measured data (sinogram) as input and automatically predicts the optimal value of the regularization parameter of the modified block Sequential Regularized Expectation Maximization (BSREM) algorithm. The proposed strategy is trained on a synthetic dataset consisting of 2D sinograms and their corresponding optimal regularization parameters. Our results demonstrate the feasibility of the approach leading to improved image reconstruction compared to classical manual tuning methods.","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":"143559821","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