IEEE Journal of Biomedical and Health Informatics最新文献

IEEE Journal of Biomedical and Health Informatics Publication Information

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-02-10 DOI: 10.1109/JBHI.2024.3523739

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Guest Editorial: Deep Medicine and AI for Health

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-02-10 DOI: 10.1109/JBHI.2024.3523751

Maria Fernanda Cabrera;Tayo Obafemi-Ajayi;Ahmed Metwally;Bobak J Mortazavi

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IEEE Journal of Biomedical and Health Informatics Information for Authors

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-02-10 DOI: 10.1109/JBHI.2024.3523743

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Sequential sEMG Recognition With Knowledge Transfer and Dynamic Graph Network Based on Spatio-Temporal Feature Extraction Network

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-02-10 DOI: 10.1109/JBHI.2024.3457026

Zhilin Li;Xianghe Chen;Jie Li;Zhongfei Bai;Hongfei Ji;Lingyu Liu;Lingjing Jin

{"title":"Sequential sEMG Recognition With Knowledge Transfer and Dynamic Graph Network Based on Spatio-Temporal Feature Extraction Network","authors":"Zhilin Li;Xianghe Chen;Jie Li;Zhongfei Bai;Hongfei Ji;Lingyu Liu;Lingjing Jin","doi":"10.1109/JBHI.2024.3457026","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3457026","url":null,"abstract":"Surface electromyography (sEMG) signals are electrical signals released by muscles during movement, which can directly reflect the muscle conditions during various actions. When a series of continuous static actions are connected along the temporal axis, a sequential action is formed, which is more aligned with people's intuitive understanding of real-life movements. The signals acquired during sequential actions are known as sequential sEMG signals, including an additional dimension of sequence, embodying richer features compared to static sEMG signals. However, existing methods show inadequate utilization of the signals' sequential characteristics. Addressing these gaps, this paper introduces the Spatio-Temporal Feature Extraction Network (STFEN), which includes a Sequential Feature Analysis Module based on static-sequential knowledge transfer, and a Spatial Feature Analysis Module based on dynamic graph networks to analyze the internal relationships between the leads. The effectiveness of STFEN is tested on both modified publicly available datasets and on our acquired Arabic Digit Sequential Electromyography (ADSE) dataset. The results show that STFEN outperforms existing models in recognizing sequential sEMG signals. Experiments have confirmed the reliability and wide applicability of STFEN in analyzing complex muscle activities. Furthermore, this work also suggests STFEN's potential benefits in rehabilitation medicine, particularly for stroke recovery, and shows promising future applications.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 2","pages":"887-899"},"PeriodicalIF":6.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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2024 Index IEEE Journal of Biomedical and Health Informatics Vol. 28 IEEE生物医学与健康信息学杂志第28卷

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-01-16 DOI: 10.1109/JBHI.2025.3530896

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IEEE Journal of Biomedical and Health Informatics Publication Information IEEE生物医学与健康信息学杂志出版信息

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-01-07 DOI: 10.1109/JBHI.2024.3512959

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IEEE Journal of Biomedical and Health Informatics Information for Authors IEEE生物医学与健康信息学杂志作者信息

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-01-07 DOI: 10.1109/JBHI.2024.3512963

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Scaling Synthetic Brain Data Generation

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-12-19 DOI: 10.1109/JBHI.2024.3520156

Mike Doan;Sergey Plis

{"title":"Scaling Synthetic Brain Data Generation","authors":"Mike Doan;Sergey Plis","doi":"10.1109/JBHI.2024.3520156","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3520156","url":null,"abstract":"The limited availability of diverse, high-quality datasets is a significant challenge in applying deep learning to neuroimaging research. Although synthetic data generation can potentially address this issue, on-the-fly generation is computationally demanding, while training on pre-generated data is inflexible and may incur high storage costs. We introduce Wirehead, a scalable in-memory data pipeline that significantly improves the performance of on-the-fly synthetic data generation for deep learning in neuroimaging. Wirehead's architecture decouples data generation from training by running multiple generators in independent parallel processes, facilitating near-linear performance gains proportional to the number of generators used. It efficiently handles terabytes of data using MongoDB, greatly minimizing prohibitive storage costs. The robust, modular design enables flexible pipeline configurations and fault-tolerant operation. We evaluated Wirehead with SynthSeg, a synthetic brain segmentation data generation tool that requires 7 days to train a model. When deployed in parallel, Wirehead achieved a near-linear 15.7x increase in throughput with 16 generators. With 20 generators, we can train a model in 9 hours instead of 7 days. This demonstrates Wirehead's ability to greatly accelerate experimentation cycles. While Wirehead represents a substantial step forward, it also reveals opportunities for future research in optimizing generation-training balance and resource allocation. Its ability to facilitate distributed deep learning has significant implications for enabling more ambitious neuroimaging research.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 2","pages":"840-847"},"PeriodicalIF":6.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Autoencoder-Based Detection of Insulin Pump Faults in Type 1 Diabetes Treatment

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-12-16 DOI: 10.1109/JBHI.2024.3518233

Elena Idi;Francesco Prendin;Giovanni Sparacino;Simone Del Favero

{"title":"Autoencoder-Based Detection of Insulin Pump Faults in Type 1 Diabetes Treatment","authors":"Elena Idi;Francesco Prendin;Giovanni Sparacino;Simone Del Favero","doi":"10.1109/JBHI.2024.3518233","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3518233","url":null,"abstract":"Individuals with type 1 diabetes (T1D) require lifelong insulin replacement to compensate for deficient endogenous insulin secretion, which would otherwise result in abnormal blood glucose levels. In recent years, significant investments have been made to improve T1D management, leading to the widespread adoption of accurate technology such as continuous glucose monitoring (CGM) sensors and automated insulin delivery systems. However, malfunctions in these devices, particularly pump systems, can cause undesirable interruptions of insulin delivery posing significant safety risks if not promptly addressed. Due to the low frequency of these episodes, developing accurate algorithms to identify insulin pump faults remains a challenge. To address these issues, this paper proposes a novel approach for detecting insulin pump faults (IPFs) by combining the ability of a long short-term memory (LSTM) autoencoder to extract features, with the strength of random forest to distinguish between anomalous and normal patterns. This method was developed and evaluated using data from 100 subjects, simulated over 90 days with the UVa/Padova T1D Simulator, an FDA-approved nonlinear computer simulator of T1D physiology. In the test set, the proposed algorithm identified the 93% of the total faults, while raising 2 false alarms in 3 months on average. These findings suggest that deep learning algorithms can enhance the safety and reliability of insulin pump systems, contributing to more effective therapeutic technologies.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 2","pages":"775-782"},"PeriodicalIF":6.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Pseudo-HFOs Elimination in iEEG Recordings Using a Robust Residual-Based Dictionary Learning Framework

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-12-12 DOI: 10.1109/JBHI.2024.3516613

Behrang Fazli Besheli;Zhiyi Sha;Amir Hossein Ayyoubi;Chandra Prakash Swamy;Thomas R. Henry;Gregory A. Worrell;Kai J. Miller;Jonathon J. Parker;David P. Darrow;Nuri Firat Ince

{"title":"Pseudo-HFOs Elimination in iEEG Recordings Using a Robust Residual-Based Dictionary Learning Framework","authors":"Behrang Fazli Besheli;Zhiyi Sha;Amir Hossein Ayyoubi;Chandra Prakash Swamy;Thomas R. Henry;Gregory A. Worrell;Kai J. Miller;Jonathon J. Parker;David P. Darrow;Nuri Firat Ince","doi":"10.1109/JBHI.2024.3516613","DOIUrl":"https://doi.org/10.1109/JBHI.2024.3516613","url":null,"abstract":"High-frequency oscillations (HFOs) in intracranial EEG (iEEG) recordings are critical biomarkers for localizing the seizure onset zone (SOZ) in patients with focal refractory epilepsy. Despite their clinical significance, HFO analysis is often compromised by high-frequency artifacts that bypass conventional detectors, resulting in false-positive events that dilute the reliability of the HFO pool. To address this challenge, this study aimed to develop an automated method to accurately identify and eliminate false-positive events, ensuring more robust and artifact-free HFO analysis for clinical applications. Using iEEG data from 15 patients with focal epilepsy, we implemented an attention-based cascaded residual dictionary learning framework coupled with a random forest classifier. Events passing an initial amplitude detector underwent a second-stage refinement to remove artifacts and non-neural noise that mimicked HFOs. This was achieved by evaluating event reconstruction quality using a dictionary learned from genuine HFOs. Compared to visual assessments by three human experts, the proposed method demonstrated 92.14% classification accuracy in distinguishing real HFOs from pseudo-HFOs. Additionally, the method improved SOZ localization accuracy in noisy iEEG data by 20% (p=6e-5) and in clean iEEG data by 4% (p=3.3e-3). The learned dictionary effectively captured raw HFO morphology in shallow layers, while deeper layers identified ripple and fast ripple components, all without human supervision. These findings highlight the algorithm's effectiveness in detecting pseudo-HFOs in corrupted iEEG data, thereby enhancing the clinical utility of HFOs as biomarkers for SOZ in epilepsy.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"29 2","pages":"857-869"},"PeriodicalIF":6.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0