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10页

Adaptive Impedance Matching with Fault Ride Through in Wireless Power Transfer for Implanted Medical Devices. 植入式医疗设备无线电力传输中的故障穿越自适应阻抗匹配。

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/EMBC53108.2024.10782376

Han Wu, Yufei Cai, Haolun Wu, Sultan Mahmud, Ali Nezaratizadeh, Adam Khalifa

{"title":"Adaptive Impedance Matching with Fault Ride Through in Wireless Power Transfer for Implanted Medical Devices.","authors":"Han Wu, Yufei Cai, Haolun Wu, Sultan Mahmud, Ali Nezaratizadeh, Adam Khalifa","doi":"10.1109/EMBC53108.2024.10782376","DOIUrl":"10.1109/EMBC53108.2024.10782376","url":null,"abstract":"IMDs has found widespread application across various medical fields. Wirelessly powered implants are increasingly being developed to interface with neurons due to its small size. The matching network (MN) within the wireless IMD is a crucial component influencing system efficiency. Conventional approaches using fixed-value MNs struggle to adapt to changes in parameters and environment. This research proposes an adaptive algorithm-based MN that enabels the system to automatically track the maximum rectified voltage despite variations in frequency and inductor, as well as sampling errors due to random external interference. For the first time, an active voltage limiter has been integrated into the MN to reject excess power in order to safeguard the chip, rather than dissipating it as heat. Implemented in TSMC 65nm technology, this system can operate under ±15% inductance fluctuation and ±10% frequency fluctuation at 500 MHz, enabling unusable systems to obtain sufficient power. The chosen proof-of-concept for this work is a neural stimulating IMD but this approach can extend beyond this setup.","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":"143559016","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

Low-Rank Constrained Reacquired-Navigator Reconstruction of multi-shot DWI. 多镜头DWI的低秩约束重采集导航重构。

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/EMBC53108.2024.10782950

Jiantai Zhou, Huabin Zhang, Penghui Luo, Changliang Wang, Fulang Qi, Jiaojiao Hu, Kecheng Yuan, Bensheng Qiu

{"title":"Low-Rank Constrained Reacquired-Navigator Reconstruction of multi-shot DWI.","authors":"Jiantai Zhou, Huabin Zhang, Penghui Luo, Changliang Wang, Fulang Qi, Jiaojiao Hu, Kecheng Yuan, Bensheng Qiu","doi":"10.1109/EMBC53108.2024.10782950","DOIUrl":"10.1109/EMBC53108.2024.10782950","url":null,"abstract":"The Diffusion-Weighted Imaging (DWI) requires additional acquisition of phase correction data and parallel imaging prescan data to respectively suppress artifacts caused by odd-even echo errors and motion-induced phase errors. In this study, we propose subtle modifications to the widely used spin-echo DW sequence, wherein an additional 180° radiofrequency refocusing pulse is applied after the completion of image echoes to acquire fully sampled navigator-echo data. Our proposed approach draws parallels with the dual spin-echo DW technique. However, our methodology distinguishes itself by utilizing positive and negative gradients to independently capture fully sampled navigator-echo data. Following this, we employ algorithms grounded in low-rank constraints, in conjunction with the reacquired navigator-echo data to address the two major phase errors inherent in Multi-Shot DWI (MSDWI). Simulation studies and in vivo brain imaging experiments demonstrate that this approach effectively suppresses image artifacts caused by the phase error, without the need for additional time-consuming prescans.","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":"143559448","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

Efficient Electromyography-Based Typing System: Towards a Novel Approach to HCI Text Input. 高效的基于肌电图的打字系统：迈向HCI文本输入的新方法。

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/EMBC53108.2024.10782422

Yi Wang, Youhao Wang, Ruilin Zhao, Yue Shi, Yingnan Bian

引用次数: 0

HeteroEEG: A Dual-Branch Spatial-Spectral-Temporal Heterogeneous Graph Network for EEG Classification. 异质脑电图：一种用于脑电图分类的双分支空间-频谱-时间异质图网络。

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/EMBC53108.2024.10781679

Zanhao Fu, Huaiyu Zhu, Ruohong Huan, Yi Zhang, Shuohui Chen, Yun Pan

{"title":"HeteroEEG: A Dual-Branch Spatial-Spectral-Temporal Heterogeneous Graph Network for EEG Classification.","authors":"Zanhao Fu, Huaiyu Zhu, Ruohong Huan, Yi Zhang, Shuohui Chen, Yun Pan","doi":"10.1109/EMBC53108.2024.10781679","DOIUrl":"10.1109/EMBC53108.2024.10781679","url":null,"abstract":"Given the non-Euclidean topology inherent in electroencephalogram (EEG) electrode configurations, graph-based approaches, particularly graph neural networks, have shown notable success across diverse EEG classification tasks. However, since the cerebral cortex lobes function individually and/or collaboratively across diverse tasks, there exist substantial differences between intra-lobe and inter-lobe brain intrinsic functional connectivity. Existing graph networks for EEG classification are based on homogeneous graphs, yet the nature of the cerebral cortex aligns more closely with a heterogeneous graph structure. To this end, we propose HeteroEEG for EEG classification, which to the best of our knowledge is the first to reframe the challenge of exploring EEG spatial information, especially decoupling different types of brain lobes and functional connections, as heterogeneous graph reasoning. Specifically, HeteroEEG is designed to be a dual-branch network aware of spatial, spectral, and temporal EEG features. Experimental results justify the superiority of HeteroEEG in pain and emotion recognition compared with other state-of-the-art studies. The heterogeneous graph construction of HeteroEEG may shed light on future graph-based EEG classification network design.","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":"143544738","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 Resistance-Free Sit-To-Stand Rehabilitative System Incorporated with Multi-Sensory Feedback. 结合多感官反馈的无阻力坐立康复系统。

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/EMBC53108.2024.10782303

Nitheezkant R, Madhav Rao

{"title":"A Resistance-Free Sit-To-Stand Rehabilitative System Incorporated with Multi-Sensory Feedback.","authors":"Nitheezkant R, Madhav Rao","doi":"10.1109/EMBC53108.2024.10782303","DOIUrl":"10.1109/EMBC53108.2024.10782303","url":null,"abstract":"Robotic rehabilitative systems have been an active area of research for all movements, including Sit to Stand (STS). STS is an important movement for performing various activities of daily living. Rehabilitation of the STS movement is one of the most challenging tasks for patients and physiotherapists alike. The existing rehabilitative systems constrain the patient to move with the system, making it difficult for the patient to eventually perform the movement independently without facing resistance from the system. This paper proposes the design of an STS rehabilitation system that assists subjects only in the parts of the motion that they fail to perform independently. The assistance is provided in a two-phase process and allows subject to attempt different levels of difficulty dynamically without having to select a target difficulty level before the start of the therapy session. The individual under test also receives real-time feedback on the movement from a multi-sensory feedback system. Post the movement, a score is generated from the system, allowing both the subject and physiotherapist to track the long-term progress of the individual under treatment.","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":"143557727","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 Wearable System for Monitoring Neurological Disorder Events with Multi-Class Classification Model in Daily Life. 基于多类分类模型的可穿戴日常生活神经障碍事件监测系统。

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/EMBC53108.2024.10782047

Yonghun Song, Inyeol Yun, Sandra Giovanoli, Chris Awai Easthope, Yoonyoung Chung

{"title":"A Wearable System for Monitoring Neurological Disorder Events with Multi-Class Classification Model in Daily Life.","authors":"Yonghun Song, Inyeol Yun, Sandra Giovanoli, Chris Awai Easthope, Yoonyoung Chung","doi":"10.1109/EMBC53108.2024.10782047","DOIUrl":"10.1109/EMBC53108.2024.10782047","url":null,"abstract":"Dysphagia and dysarthria are the prominent sequelae of neurological disorders. Treatment and rehabilitation of these impairments necessitate continuously monitoring symptoms related to swallowing and speaking. However, current medical technologies require large and diverse equipment to record these symptoms, which are predominantly limited to clinical environments. In this study, we propose an innovative wearable system for distinguishing neurological disorder events using a mechano-acoustic (MA) sensor and multi-class ensemble classification model. The MA sensor exhibits a high sensitivity to neck vibration without any interference from ambient sounds. A multi-class classification model was also developed to discern the symptoms from the recorded signals accurately. The proposed classification model is an ensemble neural network trained on waveforms and mel spectrograms. As a result, we achieve a high classification accuracy of 91.94%, surpassing the performance of previous single neural networks.","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":"143559011","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

Biofeedback Training for Balance Ability Improvement: An Analysis of Short-term Effects and Sensory Information Utilization^. 提高平衡能力的生物反馈训练：短期效果和感觉信息利用分析。

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/EMBC53108.2024.10781520

Kohei Kaminishi, Kotaro Debun, Tsukasa Okimura, Yuri Terasawa, Takaki Maeda, Jun Ota

{"title":"Biofeedback Training for Balance Ability Improvement: An Analysis of Short-term Effects and Sensory Information Utilization.","authors":"Kohei Kaminishi, Kotaro Debun, Tsukasa Okimura, Yuri Terasawa, Takaki Maeda, Jun Ota","doi":"10.1109/EMBC53108.2024.10781520","DOIUrl":"10.1109/EMBC53108.2024.10781520","url":null,"abstract":"This study investigates the short-term effects of biofeedback rehabilitation on postural balance performance, addressing a significant gap in existing research that has focused primarily on long-term outcomes. The present study aims to test the following hypothesis: Changes in the way sensory information is used through biofeedback training will lead to changes in postural balance performance in the short term. Experiments were conducted with five young, healthy individuals. Participants underwent biofeedback training sessions involving tasks such as maintaining the center of pressure of the feet within specific targets, and performed quiet standing tasks and standing tasks with both open and closed eyes before and after the training sessions.The results showed suggestive correlations between changes in sway during quiet standing and changes in sway with eyes open and closed before and after the training session, which differed between the training and control groups. This supports the hypothesis and suggests that biofeedback training may indirectly affect postural balance ability by altering the way sensory information is used and the existence of diverse strategies.Clinical Relevance- This leads to more effective biofeedback training designs based on reweighting in the use of sensory information.","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":"143559101","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. 纺织品心电图对驾驶员分心的分类。

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":"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

Effective diagnosis of sleep disorders using EEG and EOG signals. 利用脑电图和眼电图信号有效诊断睡眠障碍。

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/EMBC53108.2024.10782470

Ritika Jain, Ramakrishnan Angarai Ganesan

{"title":"Effective diagnosis of sleep disorders using EEG and EOG signals.","authors":"Ritika Jain, Ramakrishnan Angarai Ganesan","doi":"10.1109/EMBC53108.2024.10782470","DOIUrl":"10.1109/EMBC53108.2024.10782470","url":null,"abstract":"This work focuses on the diagnosis of various sleep disorders such as insomnia, narcolepsy, periodic leg movement, nocturnal frontal lobe epilepsy, bruxism, REM behavior disorder, and sleep-disordered breathing. We utilize SVM for classifying each of the sleep disorders from healthy controls. The proposed approach is evaluated on the publicly available CAP dataset comprising 108 overnight recordings from healthy controls and patients with sleep disorders. A single feature called gridded distribution entropy derived from Poincaré plots of EEG signal provides 100% accuracy in distinguishing healthy controls from each pathology, except insomnia and PLM. With the EOG channel, we are able to classify these two groups as well with 100% accuracy, demonstrating the effectiveness of EOG in disambiguating insomnia and PLM from controls.Clinical relevance- Diagnosis of sleep disorders is important to facilitate appropriate treatment. It is challenging due to the diverse nature and inter-subject variation of the physiological symptoms. Automated sleep disorder detection can improve cost efficiency and reduce variability.","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":"143559409","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

Hardware Accelerator for a Power Efficient Single-lead Dry-electrode ECG Wearable Design. 高效功率单导联干电极ECG可穿戴设计的硬件加速器。

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/EMBC53108.2024.10782919

Abdelrahman Abdou, Sridhar Krishnan

{"title":"Hardware Accelerator for a Power Efficient Single-lead Dry-electrode ECG Wearable Design.","authors":"Abdelrahman Abdou, Sridhar Krishnan","doi":"10.1109/EMBC53108.2024.10782919","DOIUrl":"10.1109/EMBC53108.2024.10782919","url":null,"abstract":"Single-lead electrocardiographic (ECG) monitoring wearables are becoming candidate technologies for long-term remote monitoring applications. Current wearable disadvantages include high power consumption from computational complex pre-processing leading to low battery life. A hardware (HW) architecture for dry electrode-based ECG signal processing to increase wearable longevity is proposed. The technology is based off an analog-front end (AFE) chip combined with a field programmable gated arrays (FPGA)-based optimized cubic Hermite interpolation approach for signal processing. This system is deployed on a FPGA board featuring a single-core processor. The architecture uses 0.01 W, utilizes 0.67% and 0.44% of available look-up-tables (LUTs) and flip-flops (FFs) components on FPGA and performed real-time signal processing. Signal quality indexes (SQIs) and signal to noise ratios (SNR) information are computed where the HW processed signals showed an average SNR of 16.4 dB. ECG R-peaks are visually identified, making this architecture suitable for heart rate (HR), and heart rate variability (HRV) estimations in long-term dry-electrode single-lead ECG monitoring 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":"143559594","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