Pub Date : 2025-10-22DOI: 10.1109/JTEHM.2025.3624469
Aaesha Alzaabi;Imran Saied;Tughrul Arslan
Objective: This study describes the design and evaluation of volunteer user trials of an unobtrusive Wi-Fi Channel State Information (CSI) vital sign sensing system in older participants aged 60 years and older in different home environments. Methods and procedures: In terms of experiment design, the implementation of user-centric sensor placement and integration informed consent with various experimental elements in the design of experiments of older people. The implemented signal processing algorithm, which extracts vital signs from the Wi-Fi CSI signal to obtain respiration and heart rate measurements, employs wavelet filtering techniques. For selecting of vital sign signals from the 52 CSI subcarriers, the Principal Component Sample Entropy (PC-SampEn) was implemented to capture the information most relevant to vital signs.Results: Two cardiorespiratory vital sign measurements were validated against wearable ground-truth devices, a respiratory belt and a photoplethysmogram (PPG). The results demonstrated an expected decrease in accuracy and measurement agreement in uncontrolled home environments.Conclusion: Although respiratory rate measurements have demonstrated promising accuracy and agreement in uncontrolled environments, heart rate measurements observed high variability in these scenarios due to challenging signal extraction. Further experiments must be conducted to address the limitation in sample size and the technical challenges in heart rate signal extraction to improve accuracy. Clinical and Translational Impact: This study provides a design of unobtrusive care technology for vital sign sensing for older adults, demonstrated and evaluated in the context of in-home monitoring for healthcare.
{"title":"Design and Evaluation of Volunteer User Trials of Unobtrusive Vital Signs Monitoring for Older People in Care Using Wi-Fi CSI Sensing","authors":"Aaesha Alzaabi;Imran Saied;Tughrul Arslan","doi":"10.1109/JTEHM.2025.3624469","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3624469","url":null,"abstract":"Objective: This study describes the design and evaluation of volunteer user trials of an unobtrusive Wi-Fi Channel State Information (CSI) vital sign sensing system in older participants aged 60 years and older in different home environments. Methods and procedures: In terms of experiment design, the implementation of user-centric sensor placement and integration informed consent with various experimental elements in the design of experiments of older people. The implemented signal processing algorithm, which extracts vital signs from the Wi-Fi CSI signal to obtain respiration and heart rate measurements, employs wavelet filtering techniques. For selecting of vital sign signals from the 52 CSI subcarriers, the Principal Component Sample Entropy (PC-SampEn) was implemented to capture the information most relevant to vital signs.Results: Two cardiorespiratory vital sign measurements were validated against wearable ground-truth devices, a respiratory belt and a photoplethysmogram (PPG). The results demonstrated an expected decrease in accuracy and measurement agreement in uncontrolled home environments.Conclusion: Although respiratory rate measurements have demonstrated promising accuracy and agreement in uncontrolled environments, heart rate measurements observed high variability in these scenarios due to challenging signal extraction. Further experiments must be conducted to address the limitation in sample size and the technical challenges in heart rate signal extraction to improve accuracy. Clinical and Translational Impact: This study provides a design of unobtrusive care technology for vital sign sensing for older adults, demonstrated and evaluated in the context of in-home monitoring for healthcare.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"480-492"},"PeriodicalIF":4.4,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: Therapeutic drug monitoring (TDM) is essential for managing medication dosages in critically ill patients, particularly for antibiotics such as vancomycin. The dynamic physiological conditions of critically ill patients require frequent monitoring of vancomycin levels to ensure therapeutic therapeutic efficacy while minimizing toxicity. Traditional Bayesian methods and pharmacokinetic (PK) models often fail because of the complex and unpredictable nature of these patients’ conditions, as well as the limitations of standard PK modeling.Methods and procedures: This study aimed to establish a gated recurrent unit (GRU)-integrated joint multilayer perceptron network (GointMLP) model to predict sequential vancomycin TDM levels in patients in the intensive care unit. The proposed model consists of three modules to maintain consistent therapeutic vancomycin concentrations while accommodating individual patient differences. By integrating regression and classification predictions, GointMLP provides a dual mechanism for clinicians to verify the reliability of predicted values for informed decision-making. Additionally, we have developed DeepTDM, a comprehensive decision support system designed for real-time vancomycin dose optimization to enhance clinical outcomes.Results: The GointMLP provides more accurate predictions compared to traditional PK models and other machine learning/deep learning approaches. This superior performance is demonstrated not only in local validation cohorts but also in the ethnically diverse MIMIC-IV dataset, validating the model’s robust generalizability.Conclusion: This work addresses the limitations of current methodologies while leveraging advancements in deep learning techniques, particularly demonstrating the effectiveness of GointMLP in enhancing patient outcomes through precise TDM. Efforts are underway to integrate DeepTDM into clinical practice, with the anticipation that it will not only support clinicians in decision-making but also substantially improve therapeutic outcomes for patients undergoing vancomycin therapy. Clinical and Translational Impact Statement: The proposed model and software enable individualized vancomycin dosing for critically ill patients, improving precision dosing and supporting seamless integration into clinical workflows
{"title":"DeepTDM: Deep Learning-Based Prediction of Sequential Therapeutic Drug Monitoring Levels of Vancomycin","authors":"Jinkyeong Park;Dohyun Kim;Donghoon Lee;Minkyu Kim;Yoon Kim;Seon-Sook Han;Yeonjeong Heo;Hyun-Soo Choi","doi":"10.1109/JTEHM.2025.3623605","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3623605","url":null,"abstract":"Objective: Therapeutic drug monitoring (TDM) is essential for managing medication dosages in critically ill patients, particularly for antibiotics such as vancomycin. The dynamic physiological conditions of critically ill patients require frequent monitoring of vancomycin levels to ensure therapeutic therapeutic efficacy while minimizing toxicity. Traditional Bayesian methods and pharmacokinetic (PK) models often fail because of the complex and unpredictable nature of these patients’ conditions, as well as the limitations of standard PK modeling.Methods and procedures: This study aimed to establish a gated recurrent unit (GRU)-integrated joint multilayer perceptron network (GointMLP) model to predict sequential vancomycin TDM levels in patients in the intensive care unit. The proposed model consists of three modules to maintain consistent therapeutic vancomycin concentrations while accommodating individual patient differences. By integrating regression and classification predictions, GointMLP provides a dual mechanism for clinicians to verify the reliability of predicted values for informed decision-making. Additionally, we have developed DeepTDM, a comprehensive decision support system designed for real-time vancomycin dose optimization to enhance clinical outcomes.Results: The GointMLP provides more accurate predictions compared to traditional PK models and other machine learning/deep learning approaches. This superior performance is demonstrated not only in local validation cohorts but also in the ethnically diverse MIMIC-IV dataset, validating the model’s robust generalizability.Conclusion: This work addresses the limitations of current methodologies while leveraging advancements in deep learning techniques, particularly demonstrating the effectiveness of GointMLP in enhancing patient outcomes through precise TDM. Efforts are underway to integrate DeepTDM into clinical practice, with the anticipation that it will not only support clinicians in decision-making but also substantially improve therapeutic outcomes for patients undergoing vancomycin therapy. Clinical and Translational Impact Statement: The proposed model and software enable individualized vancomycin dosing for critically ill patients, improving precision dosing and supporting seamless integration into clinical workflows","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"493-506"},"PeriodicalIF":4.4,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11208607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1109/JTEHM.2025.3619802
Kelly Long;Ganesh M. Babulal;Sayeh Bayat
Objective: To examine how early pathophysiological changes in Alzheimer’s disease (AD) affect navigational decision-making by analyzing the complexity of driving routes in older adults with and without preclinical AD. Methods: We developed a novel route complexity metric based on the number of left and right turns and the deviation from the most direct path, accounting for cognitive load during navigation. Naturalistic GPS driving data were collected for a year from 111 older adults aged 65–85, with preclinical AD status determined via cerebrospinal fluid amyloid biomarkers. A multiple linear regression model was used to assess the relationship between age, preclinical AD status, and route complexity. Results: The findings of this study indicate that preclinical AD may influence the navigational abilities of older adults. After controlling for age, participants with preclinical AD chose routes with higher baseline complexity than the control group. It further revealed that participants with preclinical AD selected routes with lower complexity as they aged—a trend not observed in healthy controls. Conclusion: Preclinical AD is associated with changes in spatial decision-making that are observable in real-world driving behaviours. The age-related decline in route complexity among those with preclinical AD may reflect compensatory strategies or progressive cognitive changes. Clinical Impact: This study presents a non-invasive, behaviour-based metric that could support early detection of cognitive decline. It may also inform the design of personalized mobility interventions and dementia-friendly mobility systems.
{"title":"Characterizing Navigational Changes in Preclinical Alzheimer’s Disease: A Route Complexity Metric Derived From Naturalistic Driving Data","authors":"Kelly Long;Ganesh M. Babulal;Sayeh Bayat","doi":"10.1109/JTEHM.2025.3619802","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3619802","url":null,"abstract":"Objective: To examine how early pathophysiological changes in Alzheimer’s disease (AD) affect navigational decision-making by analyzing the complexity of driving routes in older adults with and without preclinical AD. Methods: We developed a novel route complexity metric based on the number of left and right turns and the deviation from the most direct path, accounting for cognitive load during navigation. Naturalistic GPS driving data were collected for a year from 111 older adults aged 65–85, with preclinical AD status determined via cerebrospinal fluid amyloid biomarkers. A multiple linear regression model was used to assess the relationship between age, preclinical AD status, and route complexity. Results: The findings of this study indicate that preclinical AD may influence the navigational abilities of older adults. After controlling for age, participants with preclinical AD chose routes with higher baseline complexity than the control group. It further revealed that participants with preclinical AD selected routes with lower complexity as they aged—a trend not observed in healthy controls. Conclusion: Preclinical AD is associated with changes in spatial decision-making that are observable in real-world driving behaviours. The age-related decline in route complexity among those with preclinical AD may reflect compensatory strategies or progressive cognitive changes. Clinical Impact: This study presents a non-invasive, behaviour-based metric that could support early detection of cognitive decline. It may also inform the design of personalized mobility interventions and dementia-friendly mobility systems.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"471-479"},"PeriodicalIF":4.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11197551","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-23DOI: 10.1109/JTEHM.2025.3613609
Huawei Jiang;Husna Mutahira;Shibo Wei;Mannan Saeed Muhammad
Objective: The detection of heart abnormalities using electrocardiograms (ECG) is a critical task in medical diagnostics. A lot of literature has utilized ResNet and Transformer architectures to detect heart disease based on ECG signals. Recently, a new class of algorithms has emerged, challenging these established methods. A selective state space model (SSM) called Mamba has exhibited promising potential as an alternative to Transformers due to its efficient handling of longer sequences. In this context, we propose a Mamba-based model for detecting heart abnormalities, named ECG-Mamba. Recognizing that common data augmentation methods such as MixUp and CutMix do not perform well with Mamba on ECG data, we introduce a data augmentation technique called non-uniform-mix to enhance the model’s performance.Methods and procedures: ECG-Mamba is based on Vision Mamba (Vim), a variant of Mamba that utilizes a bidirectional SSM, enhancing its capability to process ECG data effectively. To address the sensitivity of the Mamba model to noise and the lack of suitable data augmentation techniques, we propose a data augmentation algorithm that conservatively introduces data augmentation by performing non-uniform operations on the dataset across different epochs. Specifically, we apply MixUp to a portion of the dataset in different epochs.Results: Experimental results indicate that ECG-Mamba outperforms the best algorithms in the PhysioNet/Computing in Cardiology (CinC) Challenges of 2020 and 2021 based on the AUPRC and AUROC, specifically with ECG-Mamba achieving an AUPRC score 16.6% higher than the best algorithm in the PhysioNet/CinC Challenge 2021 on 12-lead ECGs, reaching 0.61. Moreover, with the proposed data augmentation method Non-Uniform-Mix, ECG-Mamba’s AUPRC reached 0.6271, representing a 2.8% improvement.Conclusion: The ECG-Mamba model, based on the SSM, demonstrates potential in detecting cardiac abnormalities from ECG data. Although the model surpasses existing algorithms, it exhibits sensitivity to noise, requiring careful data augmentation. The proposed conservative data augmentation technique addresses this challenge and improves the model’s performance, suggesting a promising direction for future research in ECG analysis using SSMs. The implementation is publicly available at https://huggingface.co/poult/ECGMambaVersionOfJTEHM2020-2021_final.Clinical and Translational Impact Statement: ECG-Mamba enhances heart abnormality detection, enabling early diagnosis and personalised treatment in resource-limited and telemedicine settings. Using real-world data from the PhysioNet/CinC Challenges 2020 and 2021, it accurately models multiple concurrent cardiac conditions, reflecting complex clinical scenarios. Its conservative Non-Uniform-Mix augmentation mitigates noise sensitivity, improving accuracy and reliability for seamless integration into clinical workflows, thus supporting evidence-based practice and addressing healthcare disparities.
目的：利用心电图检测心脏异常是医学诊断中的一项重要任务。许多文献利用ResNet和Transformer架构来检测基于心电信号的心脏病。最近，一类新的算法出现了，挑战这些既定的方法。选择性状态空间模型（SSM）称为曼巴已经显示出有希望的潜力，作为替代变形金刚由于其有效的处理较长的序列。在这种情况下，我们提出了一种基于曼巴的检测心脏异常的模型，称为ecg -曼巴。认识到常见的数据增强方法如MixUp和CutMix在曼巴心电图数据上表现不佳，我们引入了一种称为非均匀混合的数据增强技术来提高模型的性能。方法和步骤：ECG-Mamba是基于视觉曼巴（Vim），曼巴的一种变体，利用双向SSM，增强其有效处理ECG数据的能力。为了解决曼巴模型对噪声的敏感性和缺乏合适的数据增强技术，我们提出了一种数据增强算法，该算法通过在不同时代的数据集上执行非均匀操作来保守地引入数据增强。具体来说，我们将MixUp应用于不同时代的部分数据集。结果：实验结果表明，在基于AUPRC和AUROC的2020年和2021年的PhysioNet/Computing in Cardiology （cinology）挑战赛中，ECG-Mamba的表现优于最佳算法，特别是在12联头心电图上，ECG-Mamba的AUPRC得分比最佳算法高出16.6%，达到0.61。此外，采用本文提出的数据增强方法Non-Uniform-Mix， ECG-Mamba的AUPRC达到0.6271，提高2.8%。结论：基于SSM的ECG- mamba模型显示了从ECG数据检测心脏异常的潜力。尽管该模型超越了现有的算法，但它对噪声很敏感，需要仔细地增强数据。所提出的保守数据增强技术解决了这一挑战，提高了模型的性能，为使用ssm进行心电分析的未来研究提供了一个有希望的方向。该技术的实施可在https://huggingface.co/poult/ECGMambaVersionOfJTEHM2020-2021_final.Clinical和转化影响声明中公开获得：ECG-Mamba增强了心脏异常检测，使资源有限和远程医疗环境中的早期诊断和个性化治疗成为可能。使用来自PhysioNet/CinC挑战2020和2021的真实世界数据，它准确地模拟了多种并发心脏病，反映了复杂的临床场景。其保守的非均匀混合增强功能减轻了噪声敏感性，提高了与临床工作流程无缝集成的准确性和可靠性，从而支持循证实践并解决医疗保健差异。
{"title":"ECG-Mamba: Cardiac Abnormality Classification With Non-Uniform-Mix Augmentation on 12-Lead ECGs","authors":"Huawei Jiang;Husna Mutahira;Shibo Wei;Mannan Saeed Muhammad","doi":"10.1109/JTEHM.2025.3613609","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3613609","url":null,"abstract":"Objective: The detection of heart abnormalities using electrocardiograms (ECG) is a critical task in medical diagnostics. A lot of literature has utilized ResNet and Transformer architectures to detect heart disease based on ECG signals. Recently, a new class of algorithms has emerged, challenging these established methods. A selective state space model (SSM) called Mamba has exhibited promising potential as an alternative to Transformers due to its efficient handling of longer sequences. In this context, we propose a Mamba-based model for detecting heart abnormalities, named ECG-Mamba. Recognizing that common data augmentation methods such as MixUp and CutMix do not perform well with Mamba on ECG data, we introduce a data augmentation technique called non-uniform-mix to enhance the model’s performance.Methods and procedures: ECG-Mamba is based on Vision Mamba (Vim), a variant of Mamba that utilizes a bidirectional SSM, enhancing its capability to process ECG data effectively. To address the sensitivity of the Mamba model to noise and the lack of suitable data augmentation techniques, we propose a data augmentation algorithm that conservatively introduces data augmentation by performing non-uniform operations on the dataset across different epochs. Specifically, we apply MixUp to a portion of the dataset in different epochs.Results: Experimental results indicate that ECG-Mamba outperforms the best algorithms in the PhysioNet/Computing in Cardiology (CinC) Challenges of 2020 and 2021 based on the AUPRC and AUROC, specifically with ECG-Mamba achieving an AUPRC score 16.6% higher than the best algorithm in the PhysioNet/CinC Challenge 2021 on 12-lead ECGs, reaching 0.61. Moreover, with the proposed data augmentation method Non-Uniform-Mix, ECG-Mamba’s AUPRC reached 0.6271, representing a 2.8% improvement.Conclusion: The ECG-Mamba model, based on the SSM, demonstrates potential in detecting cardiac abnormalities from ECG data. Although the model surpasses existing algorithms, it exhibits sensitivity to noise, requiring careful data augmentation. The proposed conservative data augmentation technique addresses this challenge and improves the model’s performance, suggesting a promising direction for future research in ECG analysis using SSMs. The implementation is publicly available at <uri>https://huggingface.co/poult/ECGMambaVersionOfJTEHM2020-2021_final</uri>.Clinical and Translational Impact Statement: ECG-Mamba enhances heart abnormality detection, enabling early diagnosis and personalised treatment in resource-limited and telemedicine settings. Using real-world data from the PhysioNet/CinC Challenges 2020 and 2021, it accurately models multiple concurrent cardiac conditions, reflecting complex clinical scenarios. Its conservative Non-Uniform-Mix augmentation mitigates noise sensitivity, improving accuracy and reliability for seamless integration into clinical workflows, thus supporting evidence-based practice and addressing healthcare disparities.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"461-470"},"PeriodicalIF":4.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-18DOI: 10.1109/JTEHM.2025.3611498
Miaoxin Ji;Hongru Dong;Lina Guo;Wen LI
Objective: Parkinson’s disease (PD) diagnosis relies on the evaluation of motor and non-motor symptoms, with gait abnormalities serving as a key marker for early detection. Traditional clinical assessment often relies on visual gait analysis, which is a subjective process prone to bias. This study introduces a PD severity classification method that leverages gait features. Methods: A Spatial-temporal Convolutional neural network-Transformer (ST-CNN-Transformer) model for PD severity classification was established. Multimodal gait data, including foot acceleration, angular velocity, and Vertical Ground Reaction Force (VGRF), were collected in collaboration with Xiangyang First People’s Hospital, Hubei Province. Zero-velocity points (ZVPs) were detected using the Generalized Likelihood Ratio Test (GLRT), and gait cycle features were extracted from inertial measurement unit data for precise segmentation. The ST-CNN-Transformer model captures spatial-temporal features and periodic correlations. Results: Evaluation on a dataset comprising 10 healthy controls and 30 PD patients yielded a classification accuracy of 98.81%, surpassing existing gait-based methods for PD severity classification. Conclusion: This study introduces a deep learning (DL) approach to automating PD severity classification by integrating ZVP and gait segmentation derived from multimodal data. The proposed model significantly enhances diagnostic accuracy. Significance: By combining DL with GLRT-based gait segmentation and multimodal gait analysis, this study proposes a robust and interpretable PD severity assessment framework that contributes to more accurate and objective clinical decision-making.
{"title":"Diagnosis and Severity Rating of Parkinson’s Disease Based on Multimodal Gait Signal Analysis With GLRT and ST-CNN-Transformer Networks","authors":"Miaoxin Ji;Hongru Dong;Lina Guo;Wen LI","doi":"10.1109/JTEHM.2025.3611498","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3611498","url":null,"abstract":"Objective: Parkinson’s disease (PD) diagnosis relies on the evaluation of motor and non-motor symptoms, with gait abnormalities serving as a key marker for early detection. Traditional clinical assessment often relies on visual gait analysis, which is a subjective process prone to bias. This study introduces a PD severity classification method that leverages gait features. Methods: A Spatial-temporal Convolutional neural network-Transformer (ST-CNN-Transformer) model for PD severity classification was established. Multimodal gait data, including foot acceleration, angular velocity, and Vertical Ground Reaction Force (VGRF), were collected in collaboration with Xiangyang First People’s Hospital, Hubei Province. Zero-velocity points (ZVPs) were detected using the Generalized Likelihood Ratio Test (GLRT), and gait cycle features were extracted from inertial measurement unit data for precise segmentation. The ST-CNN-Transformer model captures spatial-temporal features and periodic correlations. Results: Evaluation on a dataset comprising 10 healthy controls and 30 PD patients yielded a classification accuracy of 98.81%, surpassing existing gait-based methods for PD severity classification. Conclusion: This study introduces a deep learning (DL) approach to automating PD severity classification by integrating ZVP and gait segmentation derived from multimodal data. The proposed model significantly enhances diagnostic accuracy. Significance: By combining DL with GLRT-based gait segmentation and multimodal gait analysis, this study proposes a robust and interpretable PD severity assessment framework that contributes to more accurate and objective clinical decision-making.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"450-460"},"PeriodicalIF":4.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11172342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-03DOI: 10.1109/JTEHM.2025.3602158
Ata Chizari;Jan L. van der Hoek;Anne R. D. Rook;Marleen E. Krommendijk;Tess J. Snoeijink;Adrie Visser;Tom Knop;Jutta Arens;Srirang Manohar;Wiendelt Steenbergen;Erik Groot Jebbink
Objective: Advancing microcirculatory perfusion assessment methods is crucial for evaluating organ status during ex-vivo organ preservation and expanding the donor pool. This study demonstrates the feasibility of microcirculatory perfusion imaging in an ex-vivo liver model under normothermic machine perfusion, using two non-contact imaging techniques: laser Doppler perfusion imaging (LDPI) and laser speckle contrast imaging (LSCI).Methods and procedures: An ex-vivo porcine liver was perfused with oxygenated blood for 3 hours. Blood samples were collected every 30 minutes from the hepatic artery and portal vein to evaluate the liver’s overall status. Each of the five liver lobes was imaged every 15 minutes using both the in-house developed LDPI and wireless LSCI devices. Temporally averaged perfusion maps were analyzed to assess spatiotemporal blood flow. Then, correlations between LDPI and LSCI perfusion indices were evaluated.Results: Spatiotemporal perfusion images showed detailed superficial microcirculatory perfusion across five imaged lobes. High correlations between LDPI and LSCI indices were observed in lobes $3-5$ (${R}^{2}=0.81$ ), which were well-perfused. Blood lactate levels increased over time, indicating a shift in metabolic activity due to ischemia. Also, correlation of LSCI perfusion indices with pH (${R}^{2}_{max .}=0.95$ ) was observed.Conclusion: The ex-vivo liver model mimics in-vivo perfusion under controlled experimental conditions. LDPI and LSCI provide rapid, independent assessments of local microcirculatory blood flow, demonstrate a high inter-technique correlation, and reflect the overall deterioration of liver status, as evidenced by blood gas parameters.Significance: A compact, wireless LSCI system—validated against LDPI—enables non-invasive evaluation of microcirculatory status and serves as a complementary tool for assessing deep tissue viability. Clinical and Translational Impact Statement—We introduce a wireless, compact, and non-contact LSCI system (validated by LDPI) enabling microcirculatory assessment during machine perfusion, complementing deep tissue medical imaging methods and blood gas analysis to enhance organ viability evaluation and support pre-transplantation treatment decisions (Category: Pre-Clinical Research).
{"title":"Feasibility of Laser Speckle-Based Perfusion Imaging in an Ex-Vivo Liver Model","authors":"Ata Chizari;Jan L. van der Hoek;Anne R. D. Rook;Marleen E. Krommendijk;Tess J. Snoeijink;Adrie Visser;Tom Knop;Jutta Arens;Srirang Manohar;Wiendelt Steenbergen;Erik Groot Jebbink","doi":"10.1109/JTEHM.2025.3602158","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3602158","url":null,"abstract":"Objective: Advancing microcirculatory perfusion assessment methods is crucial for evaluating organ status during ex-vivo organ preservation and expanding the donor pool. This study demonstrates the feasibility of microcirculatory perfusion imaging in an ex-vivo liver model under normothermic machine perfusion, using two non-contact imaging techniques: laser Doppler perfusion imaging (LDPI) and laser speckle contrast imaging (LSCI).Methods and procedures: An ex-vivo porcine liver was perfused with oxygenated blood for 3 hours. Blood samples were collected every 30 minutes from the hepatic artery and portal vein to evaluate the liver’s overall status. Each of the five liver lobes was imaged every 15 minutes using both the in-house developed LDPI and wireless LSCI devices. Temporally averaged perfusion maps were analyzed to assess spatiotemporal blood flow. Then, correlations between LDPI and LSCI perfusion indices were evaluated.Results: Spatiotemporal perfusion images showed detailed superficial microcirculatory perfusion across five imaged lobes. High correlations between LDPI and LSCI indices were observed in lobes <inline-formula> <tex-math>$3-5$ </tex-math></inline-formula> (<inline-formula> <tex-math>${R}^{2}=0.81$ </tex-math></inline-formula>), which were well-perfused. Blood lactate levels increased over time, indicating a shift in metabolic activity due to ischemia. Also, correlation of LSCI perfusion indices with pH (<inline-formula> <tex-math>${R}^{2}_{max .}=0.95$ </tex-math></inline-formula>) was observed.Conclusion: The ex-vivo liver model mimics in-vivo perfusion under controlled experimental conditions. LDPI and LSCI provide rapid, independent assessments of local microcirculatory blood flow, demonstrate a high inter-technique correlation, and reflect the overall deterioration of liver status, as evidenced by blood gas parameters.Significance: A compact, wireless LSCI system—validated against LDPI—enables non-invasive evaluation of microcirculatory status and serves as a complementary tool for assessing deep tissue viability. Clinical and Translational Impact Statement—We introduce a wireless, compact, and non-contact LSCI system (validated by LDPI) enabling microcirculatory assessment during machine perfusion, complementing deep tissue medical imaging methods and blood gas analysis to enhance organ viability evaluation and support pre-transplantation treatment decisions (Category: Pre-Clinical Research).","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"437-449"},"PeriodicalIF":4.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Accurate identification of bone metastases in lung cancer is essential for effective diagnosis and treatment. However, existing methods for detecting bone metastases face significant limitations, particularly in whole-body bone scans, due to low resolution, blurred boundaries, and the variability in lesion shapes and sizes, which challenge traditional convolutional neural networks. Purpose: To accurately isolate the metastasized lesions from whole-body bone scans, we propose a lesion-aware segmentation model using deep learning techniques. Methods: The proposed model integrates lesion boundary-guided strategies, multi-scale learning, and image shape guidance into an encoder-decoder architecture network. This approach significantly improves segmentation performance in low-resolution and blurred boundary conditions while effectively managing lesion shape variability and mitigating interference from the rectangular format of the images. Results: Experimental evaluations conducted on clinical data of 274 whole-body bone scans demonstrate that the proposed model achieves a 7.45% improvement in the Dice Similarity Coefficient and a 11.75% improvement in Recall compared to specialized segmentation models for whole-body bone scans, achieving significant improvements and balanced performance across key metrics. Conclusions: This model offers a more accurate and efficient solution for identifying bone metastases in lung cancer, alleviating the challenges of deep learning-based automated analysis of low-resolution, large-size medical images of whole-body bone scans. The code is available at https://github.com/carorange/segmentation Clinical and Impact: This lesion-aware deep learning model provides a robust, automated solution for identifying bone metastases in low-resolution, large-scale whole-body bone scans, enabling earlier and more accurate clinical decisions and potentially improving patient outcomes in lung cancer care.
{"title":"Integrating Non-Square Filter and Boundary Enhancement Into Encoder–Decoder Network for Lesion-Aware Segmentation of Large-Size Low-Resolution Bone Scintigrams","authors":"Ailing Xie;Qiang Lin;Xianwu Zeng;Yongchun Cao;Zhengxing Man;Caihong Liu;Xiaodi Huang","doi":"10.1109/JTEHM.2025.3605042","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3605042","url":null,"abstract":"Background: Accurate identification of bone metastases in lung cancer is essential for effective diagnosis and treatment. However, existing methods for detecting bone metastases face significant limitations, particularly in whole-body bone scans, due to low resolution, blurred boundaries, and the variability in lesion shapes and sizes, which challenge traditional convolutional neural networks. Purpose: To accurately isolate the metastasized lesions from whole-body bone scans, we propose a lesion-aware segmentation model using deep learning techniques. Methods: The proposed model integrates lesion boundary-guided strategies, multi-scale learning, and image shape guidance into an encoder-decoder architecture network. This approach significantly improves segmentation performance in low-resolution and blurred boundary conditions while effectively managing lesion shape variability and mitigating interference from the rectangular format of the images. Results: Experimental evaluations conducted on clinical data of 274 whole-body bone scans demonstrate that the proposed model achieves a 7.45% improvement in the Dice Similarity Coefficient and a 11.75% improvement in Recall compared to specialized segmentation models for whole-body bone scans, achieving significant improvements and balanced performance across key metrics. Conclusions: This model offers a more accurate and efficient solution for identifying bone metastases in lung cancer, alleviating the challenges of deep learning-based automated analysis of low-resolution, large-size medical images of whole-body bone scans. The code is available at <uri>https://github.com/carorange/segmentation</uri> Clinical and Impact: This lesion-aware deep learning model provides a robust, automated solution for identifying bone metastases in low-resolution, large-scale whole-body bone scans, enabling earlier and more accurate clinical decisions and potentially improving patient outcomes in lung cancer care.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"421-436"},"PeriodicalIF":4.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11146776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-22DOI: 10.1109/JTEHM.2025.3601988
Ayman Anwar;Yassin Khalifa;Amanda S. Mahoney;Mehdy Dousty;James L. Coyle;Ervin Sejdic
Objective: Accurate tracking of anatomical landmarks during swallowing is critical for early diagnosis and treatment of dysphagia. Hyoid bone displacement plays a pivotal role in upper esophageal sphincter opening and airway protection, traditionally assessed via a videofluoroscopic swallow study (VFSS). However, VFSSs are subjective, expose patients to radiation, and are not universally accessible. High-resolution cervical auscultation (HRCA) offers a noninvasive alternative, utilizing acoustic and vibratory signals. Prior studies have validated HRCA’s efficacy in analyzing swallowing kinematics and correlating with hyoid bone displacement, typically employing transform domain characteristics and recurrent neural networks to achieve 50% overlap in predicted displacementsMethods: We introduce a transformer-based architecture for tracking hyoid bone displacement directly from raw HRCA signals, leveraging advanced temporal and spatial feature extraction methods using attention mechanism. The proposed pipeline preprocesses HRCA signals, segments individual swallows, and tracks the hyoid bone.Results: Our approach significantly improves upon existing methods, achieving over 70% relative overlap in predicted hyoid bone displacements across validation folds, surpassing state-of-the-art baseline models by a margin of at least 20%. Comprehensive statistical analysis confirms the robustness and accuracy of our predictions, demonstrating strong generalization capabilities on an independent dataset.Conclusion: This novel approach underscores the potential of transformer models in promoting noninvasive dysphagia assessment, offering a precise tracking of hyoid bone without VFSS images, and providing clinicians with insights about its movement trends, potentially aiding in clinical decision-making and bringing us one step closer to automated noninvasive swallowing assessment protocols. Clinical Impact– This study highlights the potential of automated hyoid bone tracking using HRCA signals to enhance dysphagia assessment by providing objective, noninvasive measurements that potentially support earlier detection and monitoring of swallowing impairments in both clinical and home healthcare settings, ultimately improving patient management and treatment outcomes.
{"title":"Videographic-Free Tracking of Hyoid Bone Displacement During Swallowing Using Accelerometer Signals and Transformers","authors":"Ayman Anwar;Yassin Khalifa;Amanda S. Mahoney;Mehdy Dousty;James L. Coyle;Ervin Sejdic","doi":"10.1109/JTEHM.2025.3601988","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3601988","url":null,"abstract":"Objective: Accurate tracking of anatomical landmarks during swallowing is critical for early diagnosis and treatment of dysphagia. Hyoid bone displacement plays a pivotal role in upper esophageal sphincter opening and airway protection, traditionally assessed via a videofluoroscopic swallow study (VFSS). However, VFSSs are subjective, expose patients to radiation, and are not universally accessible. High-resolution cervical auscultation (HRCA) offers a noninvasive alternative, utilizing acoustic and vibratory signals. Prior studies have validated HRCA’s efficacy in analyzing swallowing kinematics and correlating with hyoid bone displacement, typically employing transform domain characteristics and recurrent neural networks to achieve 50% overlap in predicted displacementsMethods: We introduce a transformer-based architecture for tracking hyoid bone displacement directly from raw HRCA signals, leveraging advanced temporal and spatial feature extraction methods using attention mechanism. The proposed pipeline preprocesses HRCA signals, segments individual swallows, and tracks the hyoid bone.Results: Our approach significantly improves upon existing methods, achieving over 70% relative overlap in predicted hyoid bone displacements across validation folds, surpassing state-of-the-art baseline models by a margin of at least 20%. Comprehensive statistical analysis confirms the robustness and accuracy of our predictions, demonstrating strong generalization capabilities on an independent dataset.Conclusion: This novel approach underscores the potential of transformer models in promoting noninvasive dysphagia assessment, offering a precise tracking of hyoid bone without VFSS images, and providing clinicians with insights about its movement trends, potentially aiding in clinical decision-making and bringing us one step closer to automated noninvasive swallowing assessment protocols. Clinical Impact– This study highlights the potential of automated hyoid bone tracking using HRCA signals to enhance dysphagia assessment by providing objective, noninvasive measurements that potentially support earlier detection and monitoring of swallowing impairments in both clinical and home healthcare settings, ultimately improving patient management and treatment outcomes.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"402-412"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1109/JTEHM.2025.3600110
Mohsen Nabian;Louis Atallah
Objective: In the complex landscape of ICU operations, accurate discharge decisions are crucial yet challenging, as premature discharge risks readmission and mortality while prolonged stays consume resources and heighten infection risk. The objective of this work is to develop a deep learning-based Discharge Readiness Score (DRS) model using minimal clinical features to predict ICU discharge readiness, and to highlight its application in estimating excess ICU stays for resource optimization. Methods and procedures: We utilized nearly 1.8 million ICU patient-stays from 2007–2023 across 300 US hospitals in the Philips eICU database. Six readily available features (age, mean arterial pressure, systolic pressure, heart rate, respiratory rate, and Glasgow Coma Scale) were used as inputs. A 5-layer neural network predicted patient mortality within 48 hours post-ICU discharge as a proxy for discharge readiness. The model was trained on 80% of data, validated on 10%, and tested on 10% (approximately 180,000 patients). We applied the model hourly to estimate excess ICU stays, defining excess stay as the time patients remained at low risk but continued in ICU. Results: The model achieved an AUC of 0.93 on the test set. Performance was consistent across years, ethnicities, ICU types, and admission groups. Using the model, we found that about 22% of patients had excess ICU time, with a median of 16 hours. The analysis highlighted trends over time and across ICU types, providing insights into resource utilization. Conclusion: The DRS model effectively predicts ICU discharge readiness using minimal features and can estimate excess ICU stays, aiding resource optimization. Clinical Impact— The model offers a practical tool for ICU discharge planning and resource utilization analysis, potentially improving patient outcomes and ICU operations
{"title":"A Deep Learning Model for Predicting ICU Discharge Readiness and Estimating Excess ICU Stay Duration","authors":"Mohsen Nabian;Louis Atallah","doi":"10.1109/JTEHM.2025.3600110","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3600110","url":null,"abstract":"Objective: In the complex landscape of ICU operations, accurate discharge decisions are crucial yet challenging, as premature discharge risks readmission and mortality while prolonged stays consume resources and heighten infection risk. The objective of this work is to develop a deep learning-based Discharge Readiness Score (DRS) model using minimal clinical features to predict ICU discharge readiness, and to highlight its application in estimating excess ICU stays for resource optimization. Methods and procedures: We utilized nearly 1.8 million ICU patient-stays from 2007–2023 across 300 US hospitals in the Philips eICU database. Six readily available features (age, mean arterial pressure, systolic pressure, heart rate, respiratory rate, and Glasgow Coma Scale) were used as inputs. A 5-layer neural network predicted patient mortality within 48 hours post-ICU discharge as a proxy for discharge readiness. The model was trained on 80% of data, validated on 10%, and tested on 10% (approximately 180,000 patients). We applied the model hourly to estimate excess ICU stays, defining excess stay as the time patients remained at low risk but continued in ICU. Results: The model achieved an AUC of 0.93 on the test set. Performance was consistent across years, ethnicities, ICU types, and admission groups. Using the model, we found that about 22% of patients had excess ICU time, with a median of 16 hours. The analysis highlighted trends over time and across ICU types, providing insights into resource utilization. Conclusion: The DRS model effectively predicts ICU discharge readiness using minimal features and can estimate excess ICU stays, aiding resource optimization. Clinical Impact— The model offers a practical tool for ICU discharge planning and resource utilization analysis, potentially improving patient outcomes and ICU operations","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"413-420"},"PeriodicalIF":4.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11129058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-08DOI: 10.1109/JTEHM.2025.3597088
Rumaisa Abu Hasan;Tong Boon Tang;Muhamad Saiful Bahri Yusoff;Syed Saad Azhar Ali
Background: Mental resilience is an important indicator of our defence mechanism against mental illness. The assessment of mental resilience is conventionally done using psychological questionnaires but more recently, has been investigated using neuroimaging modalities such as the Magnetic Resonance Imaging and Positron Emission Tomography. While having high spatial resolution, these modalities might not be cost-effective and accessible to serve larger populations. This pilot trial investigates the performance of electroencephalography (EEG) based system to assess mental resilience under different mental conditions.Methods: A total of sixty-eight healthy adults took part in this trial. Three types of EEG features, namely spectra, functional connectivity (FC) and effective connectivity (EC) were extracted, and their correlation with a standard resilience assessment instrument – the Connor-Davidson Resilience Scale were evaluated at resting and task conditions using stepwise regression. The features with the best goodness of fit model were then used to classify individuals into a low and high mental resilience class.Results: The EC features using phase slope index achieved the highest adjusted $R^{2}$ and the lowest root mean square error, compared to the spectral and FC features. The SVM classifiers trained with the EC features were able to recognize low mental resilience with accuracy at least 66% depending on the mental condition. Fusion of SVM scores from the eyes-closed, eyes-open and task conditions improved the classification accuracy to more than 85%.Conclusion: The pilot trial reveals the EC as the most promising EEG feature type in assessing mental resilience due to its measure of causality in brain activity, and demonstrates that the fusion of decisions among different mental conditions can help improve the recognition of low mental resilience. Findings from this trial contribute to maturing an EEG-based resilience assessment system development for workplace settings. Clinical Impact—Direct assessment using brain imaging modalities such as EEG provides a cost-effective means to assess mental resilience. To our knowledge, this is the first effort for healthy subjects. With the identified neuromarkers, the proposed solution demonstrates the potential to fuse EEG features from different mental conditions to provide accurate mental resilience assessment in workplace settings.
{"title":"Electroencephalography-Based Recognition of Low Mental Resilience Using Multi-Condition Decision-Level Fusion Approach","authors":"Rumaisa Abu Hasan;Tong Boon Tang;Muhamad Saiful Bahri Yusoff;Syed Saad Azhar Ali","doi":"10.1109/JTEHM.2025.3597088","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3597088","url":null,"abstract":"Background: Mental resilience is an important indicator of our defence mechanism against mental illness. The assessment of mental resilience is conventionally done using psychological questionnaires but more recently, has been investigated using neuroimaging modalities such as the Magnetic Resonance Imaging and Positron Emission Tomography. While having high spatial resolution, these modalities might not be cost-effective and accessible to serve larger populations. This pilot trial investigates the performance of electroencephalography (EEG) based system to assess mental resilience under different mental conditions.Methods: A total of sixty-eight healthy adults took part in this trial. Three types of EEG features, namely spectra, functional connectivity (FC) and effective connectivity (EC) were extracted, and their correlation with a standard resilience assessment instrument – the Connor-Davidson Resilience Scale were evaluated at resting and task conditions using stepwise regression. The features with the best goodness of fit model were then used to classify individuals into a low and high mental resilience class.Results: The EC features using phase slope index achieved the highest adjusted <inline-formula> <tex-math>$R^{2}$ </tex-math></inline-formula> and the lowest root mean square error, compared to the spectral and FC features. The SVM classifiers trained with the EC features were able to recognize low mental resilience with accuracy at least 66% depending on the mental condition. Fusion of SVM scores from the eyes-closed, eyes-open and task conditions improved the classification accuracy to more than 85%.Conclusion: The pilot trial reveals the EC as the most promising EEG feature type in assessing mental resilience due to its measure of causality in brain activity, and demonstrates that the fusion of decisions among different mental conditions can help improve the recognition of low mental resilience. Findings from this trial contribute to maturing an EEG-based resilience assessment system development for workplace settings. Clinical Impact—Direct assessment using brain imaging modalities such as EEG provides a cost-effective means to assess mental resilience. To our knowledge, this is the first effort for healthy subjects. With the identified neuromarkers, the proposed solution demonstrates the potential to fuse EEG features from different mental conditions to provide accurate mental resilience assessment in workplace settings.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"387-401"},"PeriodicalIF":4.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11121398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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