IEEE Journal of Translational Engineering in Health and Medicine-Jtehm最新文献

{"title":"Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening","authors":"Hesam Halvaei;Tove Hygrell;Emma Svennberg;Valentina D.A. Corino;Leif Sörnmo;Martin Stridh","doi":"10.1109/JTEHM.2024.3397739","DOIUrl":"10.1109/JTEHM.2024.3397739","url":null,"abstract":"Objective: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT.Methods: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable.Results: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. Conclusion: The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"480-487"},"PeriodicalIF":3.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10521724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925721","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}

{"title":"A Dual-Camera Eye-Tracking Platform for Rapid Real-Time Diagnosis of Acute Delirium: A Pilot Study","authors":"Ahmed Al-Hindawi;Marcela Vizcaychipi;Yiannis Demiris","doi":"10.1109/JTEHM.2024.3397737","DOIUrl":"10.1109/JTEHM.2024.3397737","url":null,"abstract":"Objective: Delirium, an acute confusional state, affects 20-80% of patients in Intensive Care Units (ICUs), one in three medically hospitalized patients, and up to 50% of all patients who have had surgery. Its development is associated with short- and long-term morbidity, and increased risk of death. Yet, we lack any rapid, objective, and automated method to diagnose delirium. Here, we detail the prospective deployment of a novel dual-camera contextual eye-tracking platform. We then use the data from this platform to contemporaneously classify delirium.Results: We recruited 42 patients, resulting in 210 (114 with delirium, 96 without) recordings of hospitalized patients in ICU across two centers, as part of a prospective multi-center feasibility pilot study. All recordings made with our platform were usable for analysis. We divided the collected data into training and validation cohorts based on the data originating center. We trained two Temporal Convolutional Network (TCN) models that can classify delirium using a pre-existing manual scoring system (Confusion Assessment Method in ICU (CAM-ICU)) as the training target. The first model uses eye movements only which achieves an Area Under the Receiver Operator Curve (AUROC) of 0.67 and a mean Average Precision (mAP) of 0.68. The second model uses the point of regard, the part of the scene the patient is looking at, and increases the AUROC to 0.76 and the mAP to 0.81. These models are the first to classify delirium using continuous non-invasive eye-tracking but will require further clinical prospective validation prior to use as a decision-support tool.Clinical impact: Eye-tracking is a biological signal that can be used to identify delirium in patients in ICU. The platform, alongside the trained neural networks, can automatically, objectively, and continuously classify delirium aiding in the early detection of the deteriorating patient. Future work is aimed at prospective evaluation and clinical translation.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"488-498"},"PeriodicalIF":3.7,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10521720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925709","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}

{"title":"Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview","authors":"Shuanglin Li;Rajesh Nair;Syed Mohsen Naqvi","doi":"10.1109/JTEHM.2024.3369764","DOIUrl":"10.1109/JTEHM.2024.3369764","url":null,"abstract":"Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder commonly seen in childhood that leads to behavioural changes in social development and communication patterns, often continues into undiagnosed adulthood due to a global shortage of psychiatrists, resulting in delayed diagnoses with lasting consequences on individual’s well-being and the societal impact. Recently, machine learning methodologies have been incorporated into healthcare systems to facilitate the diagnosis and enhance the potential prediction of treatment outcomes for mental health conditions. In ADHD detection, the previous research focused on utilizing functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG) signals, which require costly equipment and trained personnel for data collection. In recent years, speech and text modalities have garnered increasing attention due to their cost-effectiveness and non-wearable sensing in data collection. In this research, conducted in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, we gathered audio data from both ADHD patients and normal controls based on the clinically popular Diagnostic Interview for ADHD in adults (DIVA). Subsequently, we transformed the speech data into text modalities through the utilization of the Google Cloud Speech API. We extracted both acoustic and text features from the data, encompassing traditional acoustic features (e.g., MFCC), specialized feature sets (e.g., eGeMAPS), as well as deep-learned linguistic and semantic features derived from pre-trained deep learning models. These features are employed in conjunction with a support vector machine for ADHD classification, yielding promising outcomes in the utilization of audio and text data for effective adult ADHD screening. Clinical impact: This research introduces a transformative approach in ADHD diagnosis, employing speech and text analysis to facilitate early and more accessible detection, particularly beneficial in areas with limited psychiatric resources. Clinical and Translational Impact Statement: The successful application of machine learning techniques in analyzing audio and text data for ADHD screening represents a significant advancement in mental health diagnostics, paving the way for its integration into clinical settings and potentially improving patient outcomes on a broader scale.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"359-370"},"PeriodicalIF":3.4,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10445184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139980103","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}

{"title":"A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography","authors":"James Skoric;Yannick D’Mello;David V. Plant","doi":"10.1109/JTEHM.2024.3368291","DOIUrl":"10.1109/JTEHM.2024.3368291","url":null,"abstract":"Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at −15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of −19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"348-358"},"PeriodicalIF":3.4,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440609","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953785","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}

{"title":"Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome","authors":"Satya P. Singh;Sukrit Gupta;Jagath C. Rajapakse","doi":"10.1109/JTEHM.2024.3366504","DOIUrl":"10.1109/JTEHM.2024.3366504","url":null,"abstract":"Brain state classification by applying deep learning techniques on neuroimaging data has become a recent topic of research. However, unlike domains where the data is low dimensional or there are large number of available training samples, neuroimaging data is high dimensional and has few training samples. To tackle these issues, we present a sparse feedforward deep neural architecture for encoding and decoding the structural connectome of the human brain. We use a sparsely connected element-wise multiplication as the first hidden layer and a fixed transform layer as the output layer. The number of trainable parameters and the training time is significantly reduced compared to feedforward networks. We demonstrate superior performance of this architecture in encoding the structural connectome implicated in Alzheimer’s disease (AD) and Parkinson’s disease (PD) from DTI brain scans. For decoding, we propose recursive feature elimination (RFE) algorithm based on DeepLIFT, layer-wise relevance propagation (LRP), and Integrated Gradients (IG) algorithms to remove irrelevant features and thereby identify key biomarkers associated with AD and PD. We show that the proposed architecture reduces 45.1% and 47.1% of the trainable parameters compared to a feedforward DNN with an increase in accuracy by 2.6 % and 3.1% for cognitively normal (CN) vs AD and CN vs PD classification, respectively. We also show that the proposed RFE method leads to a further increase in accuracy by 2.1% and 4% for CN vs AD and CN vs PD classification, while removing approximately 90% to 95% irrelevant features. Furthermore, we argue that the biomarkers (i.e., key brain regions and connections) identified are consistent with previous literature. We show that relevancy score-based methods can yield high discriminative power and are suitable for brain decoding. We also show that the proposed approach led to a reduction in the number of trainable network parameters, an increase in classification accuracy, and a detection of brain connections and regions that were consistent with earlier studies.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"371-381"},"PeriodicalIF":3.4,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953798","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}

{"title":"Optical Imaging Demonstrates Tissue-Specific Metabolic Perturbations in Mblac1 Knockout Mice","authors":"Busenur Ceyhan;Parisa Nategh;Mehrnoosh Neghabi;Jacob A. LaMar;Shalaka Konjalwar;Peter Rodriguez;Maureen K. Hahn;Matthew Gross;Gregory Grumbar;Kenneth J. Salleng;Randy D. Blakely;Mahsa Ranji","doi":"10.1109/JTEHM.2024.3355962","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3355962","url":null,"abstract":"Objective: Metabolic changes have been extensively documented in neurodegenerative brain disorders, including Parkinson’s disease and Alzheimer’s disease (AD). Mutations in the C. elegans swip-10 gene result in dopamine (DA) dependent motor dysfunction accompanied by DA neuron degeneration. Recently, the putative human ortholog of swip-10 (MBLAC1) was implicated as a risk factor in AD, a disorder that, like PD, has been associated with mitochondrial dysfunction. Interestingly, the AD risk associated with MBLAC1 arises in subjects with cardiovascular morbidity, suggesting a broader functional insult arising from reduced MBLAC1 protein expression and one possibly linked to metabolic alterations. Methods: Our current studies, utilizing Mblac1 knockout (KO) mice, seek to determine whether mitochondrial respiration is affected in the peripheral tissues of these mice. We quantified the levels of mitochondrial coenzymes, NADH, FAD, and their redox ratio (NADH/FAD, RR) in livers and kidneys of wild-type (WT) mice and their homozygous KO littermates of males and females, using 3D optical cryo-imaging. Results: Compared to WT, the RR of livers from KO mice was significantly reduced, without an apparent sex effect, driven predominantly by significantly lower NADH levels. In contrast, no genotype and sex differences were observed in kidney samples. Serum analyses of WT and KO mice revealed significantly elevated glucose levels in young and aged KO adults and diminished cholesterol levels in the aged KOs, consistent with liver dysfunction. Discussion/Conclusion: As seen with C. elegans swip-10 mutants, loss of MBLAC1 protein results in metabolic changes that are not restricted to neural cells and are consistent with the presence of peripheral comorbidities accompanying neurodegenerative disease in cases where MBLAC1 expression changes impact risk.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"298-305"},"PeriodicalIF":3.4,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10436707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139744728","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}

{"title":"Compliant Intramedullary Stems for Joint Reconstruction","authors":"John A. Mccullough;Brandon T. Peterson;Alexander M. Upfill-Brown;Thomas J. Hardin;Jonathan B. Hopkins;Nelson F. Soohoo;Tyler R. Clites","doi":"10.1109/JTEHM.2024.3365305","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3365305","url":null,"abstract":"The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"314-327"},"PeriodicalIF":3.4,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942667","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}

{"title":"Design and In Vitro Validation of an Orthopaedic Drill Guide for Femoral Stem Revision in Total Hip Arthroplasty","authors":"Jan-Willem Klok;Jessica Groenewegen;Olivier Temmerman;Niels Van Straten;Bart Van Straten;Jenny Dankelman;Tim Horeman","doi":"10.1109/JTEHM.2024.3365300","DOIUrl":"10.1109/JTEHM.2024.3365300","url":null,"abstract":"Objective: Cemented total hip arthroplasty (THA) demonstrates superior survival rates compared to uncemented procedures. Nevertheless, most younger patients opt for uncemented THA, as removing well-fixed bone cement in the femur during revisions is complex, particularly the distal cement plug. This removal procedure often increases the risk of femoral fracture or perforation, haemorrhage and weakening bone due to poor drill control and positioning. Aim of this study was to design a novel drill guide to improve drill positioning. Methods and procedures: A novel orthopaedic drill guide was developed, featuring a compliant centralizer activated by a drill guide actuator. Bone models were prepared to assess centralizing performance. Three conditions were tested: drilling without guidance, guided drilling with centralizer activation held, and guided drilling with centralizer activation released. Deviations from the bone centre were measured at the entry and exit point of the drill. Results: In the centralizing performance test, the drill guide significantly reduced drill hole deviations in both entry and exit points compared to the control (\u0000<inline-formula> <tex-math>$p &lt; 0.05$ </tex-math></inline-formula>\u0000). The absolute deviation on the exit side of the cement plug was 10.59mm (SD 1.56) for the ‘No drill guide‘ condition, 3.02mm (SD 2.09) for ‘Drill guide – hold‘ and 2.12mm (SD 1.71) for ‘Drill guide – release‘. The compliant drill guide centralizer significantly lowered the risk of cortical bone perforation during intramedullary canal drilling in the bone models due to better control of the cement drill position. Clinical and Translational Impact Statement: The drill guide potentially reduces perioperative risks in cemented femoral stem revision. Future research should identify optimal scenarios for its application.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"340-347"},"PeriodicalIF":3.4,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10433182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139953799","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}

{"title":"Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data","authors":"Maksym A. Jopek;Krzysztof Pastuszak;Sebastian Cygert;Myron G. Best;Thomas Wurdinger;Jacek Jassem;Anna J. Żaczek;Anna Supernat","doi":"10.1109/JTEHM.2024.3360865","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3360865","url":null,"abstract":"The field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and therapy personalization. This study presents a multiclass approach based on deep learning to analyze and classify diseases based on blood platelet RNA. Its primary objective is to enhance cancer-type diagnosis in clinical settings by leveraging the power of deep learning combined with high-throughput sequencing of liquid biopsy. Ultimately, the study demonstrates the potential of this approach to accurately identify the patient’s type of cancer. Methods: The developed method classifies patients using heatmap images, generated based on gene expression arranged according to the Kyoto Encyclopedia of Genes and Genomes pathways. The images represent samples of patients with ovarian cancer, endometrial cancer, glioblastoma, non-small cell lung cancer, and sarcoma, as well as cancer patients with brain metastasis. Results: Our deep learning-based models reached 66.51% balanced accuracy when distinguishing between those 6 sites of cancer origin and 90.5% balanced accuracy on a location-specific dataset where cancer types from close locations were grouped. The developed models were further investigated with an explainable artificial intelligence-based approach (XAI) - SHAP. They returned a set of 60 genes with the highest impact on the models’ decision-making process. Conclusions: Our results show that deep-learning methods are a promising opportunity for cancer detection and could support clinicians’ decision-making process in finding the solution for the black-box problem. Clinical and Translational Impact Statement— Utilizing TEPs-based liquid biopsies and deep learning, our study offers a novel approach to early cancer detection, highlighting cancer origin. The integration of Explainable AI reinforces trust in predictive outcomes. Category: Early/Pre-Clinical Research.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"306-313"},"PeriodicalIF":3.4,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418148","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738978","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}

{"title":"Executive Functions Assessment Based on Wireless EEG and 3D Gait Analysis During Dual-Task: A Feasibility Study","authors":"Pasquale Arpaia;Renato Cuocolo;Allegra Fullin;Ludovica Gargiulo;Francesca Mancino;Nicola Moccaldi;Ersilia Vallefuoco;Paolo De Blasiis","doi":"10.1109/JTEHM.2024.3357287","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3357287","url":null,"abstract":"Executive functions (EFs) are neurocognitive processes planning and regulating daily life actions. Performance of two simultaneous tasks, requiring the same cognitive resources, lead to a cognitive fatigue. Several studies investigated cognitive-motor task and the interference during walking, highlighting an increasing risk of falls especially in elderly and people with neurological diseases. A few studies instrumentally explored relationship between activation-no-activation of two EFs (working memory and inhibition) and spatial-temporal gait parameters. Aim of our study was to detect activation of inhibition and working memory during progressive difficulty levels of cognitive tasks and spontaneous walking using, respectively, wireless electroencephalography (EEG) and 3D-gait analysis. Thirteen healthy subjects were recruited. Two cognitive tasks were performed, activating inhibition (Go-NoGo) and working memory (N-back). EEG features (absolute and relative power in different bands) and kinematic parameters (7 spatial-temporal ones and Gait Variable Score for 9 range of motion of lower limbs) were analyzed. A significant decrease of stride length and an increase of external-rotation of foot progression were found during dual task with Go-NoGo. Moreover, a significant correlation was found between the relative power in the delta band at channels Fz, C4 and progressive difficulty levels of Go-NoGo (activating inhibition) during walking, whereas working memory showed no correlation. This study reinforces the hypothesis of the prevalent involvement of inhibition with respect to working memory during dual task walking and reveals specific kinematic adaptations. The foundations for EEG-based monitoring of cognitive processes involved in gait are laid. Clinical and Translational Impact Statement: Clinical and instrumental evaluation and training of executive functions (as inhibition), during cognitive-motor task, could be useful for rehabilitation treatment of gait disorder in elderly and people with neurological disease.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"268-278"},"PeriodicalIF":3.4,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676113","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}