Pub Date : 2025-01-14DOI: 10.1109/JTEHM.2025.3529748
Saurabh Jain;Bijoy Dripta Barua Chowdhury;Jarrod M. Mosier;Vignesh Subbian;Kate Hughes;Young-Jun Son
For over 40 years, airway management simulation has been a cornerstone of medical training, aiming to reduce procedural risks for critically ill patients. However, existing simulation technologies often lack the versatility and realism needed to replicate the cognitive and physical challenges of complex airway management scenarios. We developed a novel Virtual Reality (VR)-based simulation system designed to enhance immersive airway management training and research. This system integrates physical and virtual environments with an external sensory framework to capture high-fidelity data on user performance. Advanced calibration techniques ensure precise positional tracking and realistic physics-based interactions, providing a cohesive mixed-reality experience. Validation studies conducted in a dedicated medical training center demonstrated the system’s effectiveness in replicating real-world conditions. Positional calibration accuracy was achieved within 0.1 cm, with parameter calibrations showing no significant discrepancies. Validation using Pre- and post-simulation surveys indicated positive feedback on training aspects, perceived usefulness, and ease of use. These results suggest that the system offers a significant improvement in procedural and cognitive training for high-stakes medical environments.
{"title":"Design and Development of an Integrated Virtual Reality (VR)-Based Training System for Difficult Airway Management","authors":"Saurabh Jain;Bijoy Dripta Barua Chowdhury;Jarrod M. Mosier;Vignesh Subbian;Kate Hughes;Young-Jun Son","doi":"10.1109/JTEHM.2025.3529748","DOIUrl":"https://doi.org/10.1109/JTEHM.2025.3529748","url":null,"abstract":"For over 40 years, airway management simulation has been a cornerstone of medical training, aiming to reduce procedural risks for critically ill patients. However, existing simulation technologies often lack the versatility and realism needed to replicate the cognitive and physical challenges of complex airway management scenarios. We developed a novel Virtual Reality (VR)-based simulation system designed to enhance immersive airway management training and research. This system integrates physical and virtual environments with an external sensory framework to capture high-fidelity data on user performance. Advanced calibration techniques ensure precise positional tracking and realistic physics-based interactions, providing a cohesive mixed-reality experience. Validation studies conducted in a dedicated medical training center demonstrated the system’s effectiveness in replicating real-world conditions. Positional calibration accuracy was achieved within 0.1 cm, with parameter calibrations showing no significant discrepancies. Validation using Pre- and post-simulation surveys indicated positive feedback on training aspects, perceived usefulness, and ease of use. These results suggest that the system offers a significant improvement in procedural and cognitive training for high-stakes medical environments.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"49-60"},"PeriodicalIF":3.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403882","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}
Methamphetamine use disorder (MUD) is a substance use disorder. Because MUD has become more prevalent due to the COVID-19 pandemic, alternative ways to help the efficiency of mass screening of MUD are important. Previous studies used electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (GSR) aberrations during the virtual reality (VR) induction of drug craving to accurately separate patients with MUD from the healthy controls. However, whether these abnormalities present without induction of drug-cue reactivity to enable separation between patients and healthy subjects remains unclear. Here, we propose a clinically comparable intelligent system using the fusion of 5–channel EEG, HRV, and GSR data during resting state to aid in detecting MUD. Forty-six patients with MUD and 26 healthy controls were recruited and machine learning methods were employed to systematically compare the classification results of different fusion models. The analytic results revealed that the fusion of HRV and GSR features leads to the most accurate separation rate of 79%. The use of EEG, HRV, and GSR features provides more robust information, leading to relatively similar and enhanced accuracy across different classifiers. In conclusion, we demonstrated that a clinically applicable intelligent system using resting-state EEG, ECG, and GSR features without the induction of drug cue reactivity enhances the detection of MUD. This system is easy to implement in the clinical setting and can save a lot of time on setting up and experimenting while maintaining excellent accuracy to assist in mass screening of MUD.
甲基苯丙胺使用障碍（Methamphetamine use disorder， MUD）是一种物质使用障碍。由于COVID-19大流行使MUD变得更加普遍，因此提高MUD大规模筛查效率的替代方法非常重要。先前的研究使用虚拟现实（VR）诱导药物渴望时的脑电图（EEG）、心率变异性（HRV）和皮肤电反应（GSR）畸变来准确区分MUD患者和健康对照组。然而，这些异常是否没有引起药物提示反应，从而使患者与健康受试者分离，目前尚不清楚。在这里，我们提出了一种临床可比较的智能系统，该系统使用静息状态下的5通道EEG， HRV和GSR数据融合来帮助检测MUD。选取46例MUD患者和26例健康对照者，采用机器学习方法系统比较不同融合模型的分类结果。分析结果表明，HRV和GSR特征的融合使分离准确率达到79%。EEG、HRV和GSR特征的使用提供了更鲁棒的信息，导致不同分类器之间相对相似和提高的准确性。总之，我们证明了一个临床适用的智能系统，利用静息状态EEG， ECG和GSR特征，而不诱导药物线索反应性，可以增强对MUD的检测。该系统易于在临床环境中实施，可以节省大量的设置和实验时间，同时保持良好的准确性，以协助大规模筛查MUD。
{"title":"Fusion Model Using Resting Neurophysiological Data to Help Mass Screening of Methamphetamine Use Disorder","authors":"Chun-Chuan Chen;Meng-Chang Tsai;Eric Hsiao-Kuang Wu;Shao-Rong Sheng;Jia-Jeng Lee;Yung-En Lu;Shih-Ching Yeh","doi":"10.1109/JTEHM.2024.3522356","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3522356","url":null,"abstract":"Methamphetamine use disorder (MUD) is a substance use disorder. Because MUD has become more prevalent due to the COVID-19 pandemic, alternative ways to help the efficiency of mass screening of MUD are important. Previous studies used electroencephalogram (EEG), heart rate variability (HRV), and galvanic skin response (GSR) aberrations during the virtual reality (VR) induction of drug craving to accurately separate patients with MUD from the healthy controls. However, whether these abnormalities present without induction of drug-cue reactivity to enable separation between patients and healthy subjects remains unclear. Here, we propose a clinically comparable intelligent system using the fusion of 5–channel EEG, HRV, and GSR data during resting state to aid in detecting MUD. Forty-six patients with MUD and 26 healthy controls were recruited and machine learning methods were employed to systematically compare the classification results of different fusion models. The analytic results revealed that the fusion of HRV and GSR features leads to the most accurate separation rate of 79%. The use of EEG, HRV, and GSR features provides more robust information, leading to relatively similar and enhanced accuracy across different classifiers. In conclusion, we demonstrated that a clinically applicable intelligent system using resting-state EEG, ECG, and GSR features without the induction of drug cue reactivity enhances the detection of MUD. This system is easy to implement in the clinical setting and can save a lot of time on setting up and experimenting while maintaining excellent accuracy to assist in mass screening of MUD.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"1-8"},"PeriodicalIF":3.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938217","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 : 2024-12-13DOI: 10.1109/JTEHM.2024.3516335
{"title":"IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE","authors":"","doi":"10.1109/JTEHM.2024.3516335","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3516335","url":null,"abstract":"","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"C3-C3"},"PeriodicalIF":3.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10799104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825791","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 : 2024-12-13DOI: 10.1109/JTEHM.2024.3513733
{"title":">IEEE Journal on Translational Engineering in Medicine and Biology publication information","authors":"","doi":"10.1109/JTEHM.2024.3513733","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3513733","url":null,"abstract":"","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"C2-C2"},"PeriodicalIF":3.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10799063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821245","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 : 2024-12-11DOI: 10.1109/JTEHM.2024.3507892
{"title":"List of Reviewers","authors":"","doi":"10.1109/JTEHM.2024.3507892","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3507892","url":null,"abstract":"","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"739-739"},"PeriodicalIF":3.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10794571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810324","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 : 2024-12-02DOI: 10.1109/JTEHM.2024.3510429
Qunxi Dong;Yuhang Sheng;Junru Zhu;Honghong Liu;Zhigang Li;Jingyu Liu;Yalin Wang;Bin Hu
Based on available magnetic resonance imaging (MRI) studies, hippocampal alteration is one of the hallmarks during cognitive decline. However, the longitudinal hippocampal morphometric changes during the initial cognitive decline are unclear. Exploring a validated biomarker with high clinical relevance is urgent. This work proposed an automated MRI-driven longitudinal hippocampal alteration analysis system (LHAAS), which consists of hippocampal segmentation, reconstruction, registration, multivariate morphometric feature extraction, and longitudinal analysis of hippocampal morphometric and volumetric differences between groups. LHAAS was applied on two groups: cognitive unimpaired (CU) participants who maintained cognitive unimpaired (non-Progressors), and participants who converted to MCI during the following four years (Progressors). LHAAS can detect and visualize subtle deformations in the bilateral hippocampus of CU progressors four years before they show initial cognitive decline. For CU progressors, hippocampal atrophy initially occurs at the CA1 subregion and then along with disease progression, spreading to the CA2-3 and Subiculum subregion, exhibiting a left-greater-than-right trend. The volumetric analyses showed similar results. Besides, hippocampal subregions highly correlated with clinical measurement were identified by correlation analysis. LHAAS can accurately reflect the small hippocampal subregional atrophy at preclinical AD. This proposed system can track the longitudinal hippocampal alterations in the early stages of AD and provide insights for early intervention. Clinical and Translational Impact Statement: LHAAS offers early detection of subtle hippocampal alterations at preclinical AD. This advance enables pathological research and timely interventions to potentially improve patient outcomes in clinical implementation.
{"title":"MRI-Driven Longitudinal Studies of Hippocampal Alterations During the Initial Cognitive Decline","authors":"Qunxi Dong;Yuhang Sheng;Junru Zhu;Honghong Liu;Zhigang Li;Jingyu Liu;Yalin Wang;Bin Hu","doi":"10.1109/JTEHM.2024.3510429","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3510429","url":null,"abstract":"Based on available magnetic resonance imaging (MRI) studies, hippocampal alteration is one of the hallmarks during cognitive decline. However, the longitudinal hippocampal morphometric changes during the initial cognitive decline are unclear. Exploring a validated biomarker with high clinical relevance is urgent. This work proposed an automated MRI-driven longitudinal hippocampal alteration analysis system (LHAAS), which consists of hippocampal segmentation, reconstruction, registration, multivariate morphometric feature extraction, and longitudinal analysis of hippocampal morphometric and volumetric differences between groups. LHAAS was applied on two groups: cognitive unimpaired (CU) participants who maintained cognitive unimpaired (non-Progressors), and participants who converted to MCI during the following four years (Progressors). LHAAS can detect and visualize subtle deformations in the bilateral hippocampus of CU progressors four years before they show initial cognitive decline. For CU progressors, hippocampal atrophy initially occurs at the CA1 subregion and then along with disease progression, spreading to the CA2-3 and Subiculum subregion, exhibiting a left-greater-than-right trend. The volumetric analyses showed similar results. Besides, hippocampal subregions highly correlated with clinical measurement were identified by correlation analysis. LHAAS can accurately reflect the small hippocampal subregional atrophy at preclinical AD. This proposed system can track the longitudinal hippocampal alterations in the early stages of AD and provide insights for early intervention. Clinical and Translational Impact Statement: LHAAS offers early detection of subtle hippocampal alterations at preclinical AD. This advance enables pathological research and timely interventions to potentially improve patient outcomes in clinical implementation.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"98-110"},"PeriodicalIF":3.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553306","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 : 2024-11-26DOI: 10.1109/JTEHM.2024.3506556
Lok Hua Lee;Cyrus Su Hui Ho;Yee Ling Chan;Gabrielle Wann Nii Tay;Cheng-Kai Lu;Tong Boon Tang
While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders’ group. The first few components that sum up to 99% of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement—The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizable
{"title":"Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA","authors":"Lok Hua Lee;Cyrus Su Hui Ho;Yee Ling Chan;Gabrielle Wann Nii Tay;Cheng-Kai Lu;Tong Boon Tang","doi":"10.1109/JTEHM.2024.3506556","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3506556","url":null,"abstract":"While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders’ group. The first few components that sum up to 99% of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement—The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizable","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"13 ","pages":"9-22"},"PeriodicalIF":3.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10767732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993375","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 : 2024-11-20DOI: 10.1109/JTEHM.2024.3503498
Thomas M. Morin;Nick Allan;Joshua Coutts;Jacob M. Hooker;Morgan Langille;Arron Metcalfe;Andrew Thamboo;James Jackson;Manu Sharma;Tim Rees;Kenza Enright;Ken Irving
Focal intranasal drug delivery to the olfactory cleft is a promising avenue for pharmaceuticals targeting the brain. However, traditional nasal sprays often fail to deliver enough medication to this specific area. We present a laminar fluid ejection (LFE) method for precise delivery of medications to the olfactory cleft. Using a 3D-printed model of the nasal passages, we determined the precise velocity and angle of insertion needed to deposit fluid at the olfactory cleft. Then, we conducted three proof-of-concept in-vivo imaging studies to confirm olfactory delivery in humans. First, we used Technetium-99 (a radiolabeled tracer) and methylene blue (a laboratory-made dye) to visualize olfactory deposition. Both tracers showed successful deposition. In a separate study, we used functional MRI (fMRI), to compare our LFE method with a conventional nasal spray while delivering insulin. From the fMRI results, we qualitatively observed focal decreases in brain activity in prefrontal cortex following insulin delivery. Overall, these preliminary results suggest that LFE offers a targeted approach to olfactory drug delivery, opening opportunities for access to the brain.Clinical and Translational Impact Statement - Focal deposition at the olfactory cleft is a promising target for delivering medication to the brain. We present in-human tests of a laminar fluid ejection method for intranasal drug delivery and demonstrate improvements over conventional nasal spray.
{"title":"Laminar Fluid Ejection for Olfactory Drug Delivery: A Proof of Concept Study","authors":"Thomas M. Morin;Nick Allan;Joshua Coutts;Jacob M. Hooker;Morgan Langille;Arron Metcalfe;Andrew Thamboo;James Jackson;Manu Sharma;Tim Rees;Kenza Enright;Ken Irving","doi":"10.1109/JTEHM.2024.3503498","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3503498","url":null,"abstract":"Focal intranasal drug delivery to the olfactory cleft is a promising avenue for pharmaceuticals targeting the brain. However, traditional nasal sprays often fail to deliver enough medication to this specific area. We present a laminar fluid ejection (LFE) method for precise delivery of medications to the olfactory cleft. Using a 3D-printed model of the nasal passages, we determined the precise velocity and angle of insertion needed to deposit fluid at the olfactory cleft. Then, we conducted three proof-of-concept in-vivo imaging studies to confirm olfactory delivery in humans. First, we used Technetium-99 (a radiolabeled tracer) and methylene blue (a laboratory-made dye) to visualize olfactory deposition. Both tracers showed successful deposition. In a separate study, we used functional MRI (fMRI), to compare our LFE method with a conventional nasal spray while delivering insulin. From the fMRI results, we qualitatively observed focal decreases in brain activity in prefrontal cortex following insulin delivery. Overall, these preliminary results suggest that LFE offers a targeted approach to olfactory drug delivery, opening opportunities for access to the brain.Clinical and Translational Impact Statement - Focal deposition at the olfactory cleft is a promising target for delivering medication to the brain. We present in-human tests of a laminar fluid ejection method for intranasal drug delivery and demonstrate improvements over conventional nasal spray.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"727-738"},"PeriodicalIF":3.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777614","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 : 2024-11-13DOI: 10.1109/JTEHM.2024.3497895
Farnaz Khodami;Amanda S. Mahoney;James L. Coyle;Ervin Sejdić
Patients with nasogastric (NG) tubes require careful monitoring due to the potential impact of the tube on their ability to swallow safely. This study aimed to investigate the utility of high-resolution cervical auscultation (HRCA) signals in assessing swallowing functionality of patients using feeding tubes. HRCA, capturing swallowing vibratory and acoustic signals, has been explored as a surrogate for videofluoroscopy image analysis in previous research. In this study, we analyzed HRCA signals recorded from patients with NG tubes to identify swallowing kinematic events within this group of subjects. Machine learning architectures from prior research endeavors, originally designed for participants without NG tubes, were fine-tuned to accomplish three tasks in the target population: estimating the duration of upper esophageal sphincter opening, estimating the duration of laryngeal vestibule closure, and tracking the hyoid bone. The convolutional recurrent neural network proposed for the first task predicted the onset of upper esophageal sphincter opening and closure for 67.61% and 82.95% of patients, respectively, with an error margin of fewer than three frames. The hybrid model employed for the second task successfully predicted the onset of laryngeal vestibule closure and reopening for 79.62% and 75.80% of patients, respectively, with the same error margin. The stacked recurrent neural network identified hyoid bone position in test frames, achieving a 41.27% overlap with ground-truth outputs. By applying established algorithms to an unseen population, we demonstrated the utility of HRCA signals for swallowing assessment in individuals with NG tubes and showcased the generalizability of algorithms developed in our previous studies. Clinical impact: This study highlights the promise of HRCA signals for assessing swallowing in patients with NG tubes, potentially improving diagnosis, management, and care integration in both clinical and home healthcare settings.
由于鼻胃管（NG）对患者的安全吞咽能力有潜在影响，因此需要对患者进行仔细监测。本研究旨在探讨高分辨率颈部听诊（HRCA）信号在评估使用喂食管患者吞咽功能方面的实用性。HRCA 可捕捉吞咽振动和声音信号，在以前的研究中已被探索用作视频荧光镜图像分析的替代物。在这项研究中，我们分析了 NG 管患者记录的 HRCA 信号，以识别这组受试者的吞咽运动事件。之前研究中的机器学习架构原本是为没有 NG 管的受试者设计的，我们对其进行了微调，以完成目标人群的三项任务：估计食管上括约肌张开的持续时间、估计喉前庭关闭的持续时间以及跟踪舌骨。针对第一项任务提出的卷积递归神经网络分别为 67.61% 和 82.95% 的患者预测了食管上括约肌张开和闭合的开始时间，误差范围小于三帧。第二个任务采用的混合模型分别成功预测了 79.62% 和 75.80% 患者的喉前庭闭合和重新开放，误差幅度相同。堆叠递归神经网络能识别测试帧中的舌骨位置，与地面实况输出的重叠率为 41.27%。通过将已建立的算法应用于未见过的人群，我们证明了 HRCA 信号在 NG 管患者吞咽评估中的实用性，并展示了我们之前研究中开发的算法的通用性。临床影响：本研究强调了 HRCA 信号在评估 NG 管患者吞咽功能方面的前景，有可能改善临床和家庭医疗环境中的诊断、管理和护理整合。
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Pub Date : 2024-11-11DOI: 10.1109/JTEHM.2024.3496196
Paul S. Addison;Andre Antunes;Dean Montgomery;Ulf R. Borg
The inhalation-exhalation (I:E) ratio, known to be an indicator of respiratory disease, is the ratio between the inhalation phase and exhalation phase of each breath. Here, we report on results from a non-contact monitoring method for the determination of the I:E ratio. This employs a depth sensing camera system that requires no sensors to be physically attached to the patient. A range of I:E ratios from 0.3 to 1.0 over a range of respiratory rates from 4 to 40 breaths/min were generated by healthy volunteers, producing a total of 3,882 separate breaths for analysis. Depth information was acquired using an Intel D415 RealSense camera placed at 1.1 m from the subjects’ torso. This data was processed in real-time to extract depth changes within the subjects’ torso region corresponding to respiratory activity. This was further converted into a respiratory signal from which the I:E ratio was determined (I:E $_{mathrm {depth}}$ ). I:Edepth was compared to spirometer data (I:E $_{mathrm {spiro}}$ ). A Bland Altman analysis produced a mean bias of –0.004, with limits of agreement [–0.234, 0.227]. A linear regression analysis produced a line of best fit given by I:E $_{mathrm {depth}} = 1.004times $ I:Espiro – 0.006, with 95% confidence intervals for the slope [0.988, 1.019] and intercept [–0.017, 0.004]. We have demonstrated the viability of a non-contact monitoring method for determining the I:E ratio on healthy subjects breathing without mechanical support. This measure may be useful in monitoring the deterioration in respiratory status and/or response to therapy within the patient population. Clinical and Translational Impact Statement - The I:E ratio is an indicator of disease severity in COPD and asthma. Non-contact continuous monitoring of I:E ratio will offer the clinician a powerful new tool for respiratory monitoring
{"title":"Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects","authors":"Paul S. Addison;Andre Antunes;Dean Montgomery;Ulf R. Borg","doi":"10.1109/JTEHM.2024.3496196","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3496196","url":null,"abstract":"The inhalation-exhalation (I:E) ratio, known to be an indicator of respiratory disease, is the ratio between the inhalation phase and exhalation phase of each breath. Here, we report on results from a non-contact monitoring method for the determination of the I:E ratio. This employs a depth sensing camera system that requires no sensors to be physically attached to the patient. A range of I:E ratios from 0.3 to 1.0 over a range of respiratory rates from 4 to 40 breaths/min were generated by healthy volunteers, producing a total of 3,882 separate breaths for analysis. Depth information was acquired using an Intel D415 RealSense camera placed at 1.1 m from the subjects’ torso. This data was processed in real-time to extract depth changes within the subjects’ torso region corresponding to respiratory activity. This was further converted into a respiratory signal from which the I:E ratio was determined (I:E\u0000<inline-formula> <tex-math>$_{mathrm {depth}}$ </tex-math></inline-formula>\u0000). I:Edepth was compared to spirometer data (I:E\u0000<inline-formula> <tex-math>$_{mathrm {spiro}}$ </tex-math></inline-formula>\u0000). A Bland Altman analysis produced a mean bias of –0.004, with limits of agreement [–0.234, 0.227]. A linear regression analysis produced a line of best fit given by I:E\u0000<inline-formula> <tex-math>$_{mathrm {depth}} = 1.004times $ </tex-math></inline-formula>\u0000 I:Espiro – 0.006, with 95% confidence intervals for the slope [0.988, 1.019] and intercept [–0.017, 0.004]. We have demonstrated the viability of a non-contact monitoring method for determining the I:E ratio on healthy subjects breathing without mechanical support. This measure may be useful in monitoring the deterioration in respiratory status and/or response to therapy within the patient population. Clinical and Translational Impact Statement - The I:E ratio is an indicator of disease severity in COPD and asthma. Non-contact continuous monitoring of I:E ratio will offer the clinician a powerful new tool for respiratory monitoring","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"721-726"},"PeriodicalIF":3.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736214","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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