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。
{"title":"Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients","authors":"Farnaz Khodami;Amanda S. Mahoney;James L. Coyle;Ervin Sejdić","doi":"10.1109/JTEHM.2024.3497895","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3497895","url":null,"abstract":"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.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"711-720"},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713771","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 : 2024-11-11DOI: 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}
Pub Date : 2024-11-04DOI: 10.1109/JTEHM.2024.3491612
Dong Miao;Ying Zhao;Xue Ren;Meng Dou;Yu Yao;Yiran Xu;Yingchao Cui;Ailian Liu
To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.This research presents a novel approach to automating liver segmentation by identifying seven key anatomical landmarks using portal venous phase images from contrast-enhanced magnetic resonance imaging (CE-MRI). By employing a multi-task learning framework, we synchronized the detection of these landmarks with the segmentation process, resulting in accurate and robust delineation of the Couinaud segments.To comprehensively validate our model, we included multiple patient types in our test set—those with normal livers, diffuse liver diseases, and localized liver lesions—under varied imaging conditions, including two field strengths, two devices, and two contrast agents. Our model achieved an average Dice Similarity Coefficient (DSC) of 85.29%, surpassing the next best-performing models by 3.12%.Our research presents a pioneering automated approach for segmenting Couinaud segments using CE-MRI. By correlating landmark detection with segmentation, we enhance surgical planning precision. This method promises improved clinical outcomes by accurately adapting to anatomical variability and reducing potential postoperative complications.Clinical impact: The application of this technique in clinical settings is poised to enhance the precision of liver surgical planning. This could lead to more tailored surgical interventions, minimization of operative risks, and preservation of healthy liver tissue, culminating in improved patient outcomes and potentially lowering the incidence of postoperative complications.Clinical and Translational Impact Statement: This research offers a novel automated liver segmentation technique, enhancing preoperative planning and potentially reducing complications, which may translate into better postoperative outcomes in hepatic surgery.
{"title":"A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation","authors":"Dong Miao;Ying Zhao;Xue Ren;Meng Dou;Yu Yao;Yiran Xu;Yingchao Cui;Ailian Liu","doi":"10.1109/JTEHM.2024.3491612","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3491612","url":null,"abstract":"To achieve precise Couinaud liver segmentation in preoperative planning for hepatic surgery, accommodating the complex anatomy and significant variations, optimizing surgical approaches, reducing postoperative complications, and preserving liver function.This research presents a novel approach to automating liver segmentation by identifying seven key anatomical landmarks using portal venous phase images from contrast-enhanced magnetic resonance imaging (CE-MRI). By employing a multi-task learning framework, we synchronized the detection of these landmarks with the segmentation process, resulting in accurate and robust delineation of the Couinaud segments.To comprehensively validate our model, we included multiple patient types in our test set—those with normal livers, diffuse liver diseases, and localized liver lesions—under varied imaging conditions, including two field strengths, two devices, and two contrast agents. Our model achieved an average Dice Similarity Coefficient (DSC) of 85.29%, surpassing the next best-performing models by 3.12%.Our research presents a pioneering automated approach for segmenting Couinaud segments using CE-MRI. By correlating landmark detection with segmentation, we enhance surgical planning precision. This method promises improved clinical outcomes by accurately adapting to anatomical variability and reducing potential postoperative complications.Clinical impact: The application of this technique in clinical settings is poised to enhance the precision of liver surgical planning. This could lead to more tailored surgical interventions, minimization of operative risks, and preservation of healthy liver tissue, culminating in improved patient outcomes and potentially lowering the incidence of postoperative complications.Clinical and Translational Impact Statement: This research offers a novel automated liver segmentation technique, enhancing preoperative planning and potentially reducing complications, which may translate into better postoperative outcomes in hepatic surgery.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"697-710"},"PeriodicalIF":3.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742419","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636504","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 : 2024-10-30DOI: 10.1109/JTEHM.2024.3488523
Soodeh Ahani;Nikoo Niknafs;Pascal M. Lavoie;Liisa Holsti;Guy A. Dumont
Objective: Non-contact respiratory rate estimation (RR) is highly desirable for infants because of their sensitive skin. We propose a novel RGB video-based RR estimation method for infants in the neonatal intensive care unit (NICU) that can accurately measure the RR contact-less.Methods and Procedures: We utilize Eulerian video magnification (EVM) method and develop an adaptive peak prominence threshold value estimation method to address challenges of RR estimation (e.g., dark environments, shallow breathing, babies swaddled or under blankets). We recruited 13 infants recorded for 4 consecutive hours per case. We then evaluate the performance of the algorithm for several (i.e., 19 to 25) randomly selected videos, each lasting 1 minute, for each case.Results: Intraclass correlation coefficients of the proposed method over manually and automatically selected ROIs are 0.91 (95%CI: $0.89-0.93$ ) and 0.88 (95%CI: $0.85-0.9$ ), indicating excellent and good reliability, respectively. The Bland-Altman analysis of the proposed algorithm shows higher agreement between the estimated values via the proposed method and visually counted RR than the agreement between the RR obtained from the impedance sensors and reference RR, and agreement between a former EVM-based method and reference RR values.Conclusion: Our algorithm shows promising results for RR estimation in a real-life NICU environment under various conditions that can confound the estimation.Clinical impact: We present a robust algorithm for non-contact neonatal respiratory rate monitoring, capable of performing well under various environmental lighting conditions in NICU, even when the infant is clothed or covered.
{"title":"Video-Based Respiratory Rate Estimation for Infants in the NICU","authors":"Soodeh Ahani;Nikoo Niknafs;Pascal M. Lavoie;Liisa Holsti;Guy A. Dumont","doi":"10.1109/JTEHM.2024.3488523","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3488523","url":null,"abstract":"Objective: Non-contact respiratory rate estimation (RR) is highly desirable for infants because of their sensitive skin. We propose a novel RGB video-based RR estimation method for infants in the neonatal intensive care unit (NICU) that can accurately measure the RR contact-less.Methods and Procedures: We utilize Eulerian video magnification (EVM) method and develop an adaptive peak prominence threshold value estimation method to address challenges of RR estimation (e.g., dark environments, shallow breathing, babies swaddled or under blankets). We recruited 13 infants recorded for 4 consecutive hours per case. We then evaluate the performance of the algorithm for several (i.e., 19 to 25) randomly selected videos, each lasting 1 minute, for each case.Results: Intraclass correlation coefficients of the proposed method over manually and automatically selected ROIs are 0.91 (95%CI: \u0000<inline-formula> <tex-math>$0.89-0.93$ </tex-math></inline-formula>\u0000) and 0.88 (95%CI: \u0000<inline-formula> <tex-math>$0.85-0.9$ </tex-math></inline-formula>\u0000), indicating excellent and good reliability, respectively. The Bland-Altman analysis of the proposed algorithm shows higher agreement between the estimated values via the proposed method and visually counted RR than the agreement between the RR obtained from the impedance sensors and reference RR, and agreement between a former EVM-based method and reference RR values.Conclusion: Our algorithm shows promising results for RR estimation in a real-life NICU environment under various conditions that can confound the estimation.Clinical impact: We present a robust algorithm for non-contact neonatal respiratory rate monitoring, capable of performing well under various environmental lighting conditions in NICU, even when the infant is clothed or covered.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"684-696"},"PeriodicalIF":3.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594967","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 : 2024-10-01DOI: 10.1109/JTEHM.2024.3471468
Qiuyang Lin;Haocun Wang;Dwaipayan Biswas;Zheyi Li;Erika Lutin;Chris van Hoof;Mingyi Chen;Nick van Helleputte
Advancements in integrated circuit (IC) technology have accelerated the miniaturization of body-worn sensors and systems, enabling long-term health monitoring. Wearable electrocardiogram (ECG), finger photoplethysmogram (PPG), and wrist-worn PPG have shown great success and significantly improved life quality. Chest-based PPG has the potential to extract multiple vital signs but requires ultra-high dynamic range (DR) IC to read out the small PPG signal among large respiration and artifacts inherent in daily life. This paper presents a dedicated high DR system for wearable chest PPG applications with a small form factor. The whole measurement system is integrated on a 20 cm2 PCB board. We have formulated a comprehensive evaluation protocol to validate the system with on-body chest PPG measurement in the workspace environment. First, chest PPG data was obtained from 6 adults and compared to data from a standard ECG patch. This system showed an average absolute deviation (AD) of 0.41 beats per minute, achieving > 99.53% heart rate (HR) accuracy. Second, chest PPG was recorded and compared to conventional PPG finger clip and PPG wristband, also showing > 98.6% HR matching and an absolute deviation in the standard deviation of NN intervals (SDNN) of < 12.8 ms for HRV monitoring within the protocol. Moreover, it successfully derives other vital parameters such as respiration rate and blood oxygen level (SpO2), showing the advancement among all these three reference modalities. This system can pave the way for new application areas, such as chest patches, to monitor chronic heart and respiratory diseases.
{"title":"A Novel Chest-Based PPG Measurement System","authors":"Qiuyang Lin;Haocun Wang;Dwaipayan Biswas;Zheyi Li;Erika Lutin;Chris van Hoof;Mingyi Chen;Nick van Helleputte","doi":"10.1109/JTEHM.2024.3471468","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3471468","url":null,"abstract":"Advancements in integrated circuit (IC) technology have accelerated the miniaturization of body-worn sensors and systems, enabling long-term health monitoring. Wearable electrocardiogram (ECG), finger photoplethysmogram (PPG), and wrist-worn PPG have shown great success and significantly improved life quality. Chest-based PPG has the potential to extract multiple vital signs but requires ultra-high dynamic range (DR) IC to read out the small PPG signal among large respiration and artifacts inherent in daily life. This paper presents a dedicated high DR system for wearable chest PPG applications with a small form factor. The whole measurement system is integrated on a 20 cm2 PCB board. We have formulated a comprehensive evaluation protocol to validate the system with on-body chest PPG measurement in the workspace environment. First, chest PPG data was obtained from 6 adults and compared to data from a standard ECG patch. This system showed an average absolute deviation (AD) of 0.41 beats per minute, achieving > 99.53% heart rate (HR) accuracy. Second, chest PPG was recorded and compared to conventional PPG finger clip and PPG wristband, also showing > 98.6% HR matching and an absolute deviation in the standard deviation of NN intervals (SDNN) of < 12.8 ms for HRV monitoring within the protocol. Moreover, it successfully derives other vital parameters such as respiration rate and blood oxygen level (SpO2), showing the advancement among all these three reference modalities. This system can pave the way for new application areas, such as chest patches, to monitor chronic heart and respiratory diseases.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"675-683"},"PeriodicalIF":3.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10701507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524193","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 : 2024-09-19DOI: 10.1109/JTEHM.2024.3463720
Jiwon Chung;Sunghun Kim;Ji Hye Won;Hyunjin Park
Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), a framework we expand to 1) encompass multiple imaging modalities and 2) promote the simultaneous identification of structurally linked features across imaging modalities. The structurally linked brain regions were assessed using diffusion tensor imaging, which quantifies the presence of neuronal fibers, thereby grounding our approach in biologically well-founded prior knowledge within the SCCA model. In our proposed structurally linked SCCA framework, we leverage T1-weighted MRI and functional MRI (fMRI) time series data to delineate both the structural and functional characteristics of the brain. Genetic variations, specifically single nucleotide polymorphisms (SNPs), are also incorporated as a genetic modality. Validation of our methodology was conducted using a simulated dataset and large-scale normative data from the Human Connectome Project (HCP). Our approach demonstrated superior performance compared to existing methods on simulated data and revealed interpretable gene-imaging associations in the real dataset. Thus, our methodology lays the groundwork for elucidating the genetic underpinnings of brain structure and function, thereby providing novel insights into the field of neuroscience. Our code is available at https://github.com/mungegg .
{"title":"Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach","authors":"Jiwon Chung;Sunghun Kim;Ji Hye Won;Hyunjin Park","doi":"10.1109/JTEHM.2024.3463720","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3463720","url":null,"abstract":"Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), a framework we expand to 1) encompass multiple imaging modalities and 2) promote the simultaneous identification of structurally linked features across imaging modalities. The structurally linked brain regions were assessed using diffusion tensor imaging, which quantifies the presence of neuronal fibers, thereby grounding our approach in biologically well-founded prior knowledge within the SCCA model. In our proposed structurally linked SCCA framework, we leverage T1-weighted MRI and functional MRI (fMRI) time series data to delineate both the structural and functional characteristics of the brain. Genetic variations, specifically single nucleotide polymorphisms (SNPs), are also incorporated as a genetic modality. Validation of our methodology was conducted using a simulated dataset and large-scale normative data from the Human Connectome Project (HCP). Our approach demonstrated superior performance compared to existing methods on simulated data and revealed interpretable gene-imaging associations in the real dataset. Thus, our methodology lays the groundwork for elucidating the genetic underpinnings of brain structure and function, thereby providing novel insights into the field of neuroscience. Our code is available at \u0000<uri>https://github.com/mungegg</uri>\u0000.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"659-667"},"PeriodicalIF":3.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434622","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 : 2024-09-10DOI: 10.1109/JTEHM.2024.3457593
Jun Wang;Zeyang Dai;Xiao Liu
Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical limitations, such as cumbersome hardware, assuming individual bladder shapes being universal, and being sensitive to sensor placement error. Methods: A low-voltage ultrasound system with machine learning has been developed in estimating bladder filling status. A custom-made flexible 1D transducer array has been excited by low-voltage coded pulses with a pulse compression technique for an enhanced signal-to-noise ratio. In order to minimize the negative influence of possible transducer misplacement, a multiple-position training strategy using machine learning has been adopted in this work. Three popular classification methods, KNN, SVM and sparse coding, have been utilized to classify the acquired different volumes ranging from 100 ml to 300 ml into two categories: low volume and high volume. The low-volume category requires no further action while the high-volume category triggers an alarm to alert the child and caregiver. Results: When the sensor placement is ideal, i.e., the position of the practical sensor placement is on spot with the trained position, the precision and recall of the classification using sparse coding are $0.957~pm ~0.02$ and $0.958~pm ~0.02$ , respectively. Even if the transducer array is misplaced by up to 4.5 mm away from the ideal location, the proposed system is able to maintain high classification accuracy (precision $ge 0.75$ and recall $ge 0.75$ ). Category: Early/Pre-Clinical Research Clinical and Translational Impact: The proposed ultrasound sensor system for nocturnal enuresis is of significant clinical and translational value as it addresses two major issues that limit the wide adoption of similar devices. Firstly, it offers enhanced safety as the entire system has been implemented in the lowvoltage domain. Secondly, the system features ample tolerance to sensor misplacement while maintaining high classification accuracy. These features combined provide a much more user-friendly environment for children and their caregivers than existing devices.
{"title":"A Pre-Voiding Alarm System Using Wearable Ultrasound and Machine Learning Algorithms for Children With Nocturnal Enuresis","authors":"Jun Wang;Zeyang Dai;Xiao Liu","doi":"10.1109/JTEHM.2024.3457593","DOIUrl":"10.1109/JTEHM.2024.3457593","url":null,"abstract":"Nocturnal enuresis is a bothersome condition that affects many children and their caregivers. Post-voiding systems is of little value in training a child into a correct voiding routing while existing pre-voiding systems suffer from several practical limitations, such as cumbersome hardware, assuming individual bladder shapes being universal, and being sensitive to sensor placement error. Methods: A low-voltage ultrasound system with machine learning has been developed in estimating bladder filling status. A custom-made flexible 1D transducer array has been excited by low-voltage coded pulses with a pulse compression technique for an enhanced signal-to-noise ratio. In order to minimize the negative influence of possible transducer misplacement, a multiple-position training strategy using machine learning has been adopted in this work. Three popular classification methods, KNN, SVM and sparse coding, have been utilized to classify the acquired different volumes ranging from 100 ml to 300 ml into two categories: low volume and high volume. The low-volume category requires no further action while the high-volume category triggers an alarm to alert the child and caregiver. Results: When the sensor placement is ideal, i.e., the position of the practical sensor placement is on spot with the trained position, the precision and recall of the classification using sparse coding are \u0000<inline-formula> <tex-math>$0.957~pm ~0.02$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$0.958~pm ~0.02$ </tex-math></inline-formula>\u0000, respectively. Even if the transducer array is misplaced by up to 4.5 mm away from the ideal location, the proposed system is able to maintain high classification accuracy (precision \u0000<inline-formula> <tex-math>$ge 0.75$ </tex-math></inline-formula>\u0000 and recall \u0000<inline-formula> <tex-math>$ge 0.75$ </tex-math></inline-formula>\u0000). Category: Early/Pre-Clinical Research Clinical and Translational Impact: The proposed ultrasound sensor system for nocturnal enuresis is of significant clinical and translational value as it addresses two major issues that limit the wide adoption of similar devices. Firstly, it offers enhanced safety as the entire system has been implemented in the lowvoltage domain. Secondly, the system features ample tolerance to sensor misplacement while maintaining high classification accuracy. These features combined provide a much more user-friendly environment for children and their caregivers than existing devices.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"643-658"},"PeriodicalIF":3.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10671589","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181839","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 : 2024-09-10DOI: 10.1109/JTEHM.2024.3455941
George Oliveira Barros;José Nathan Andrade Muller da Silva;Henrique Machado de Sousa Proença;Stanley Almeida Araújo;David Campos Wanderley;Luciano Rebouças de Oliveira;Washington Luis Conrado Dos-Santos;Angelo Amancio Duarte;Flavio de Barros Vidal
Podocyte degenerative changes are common in various kidney diseases, and their accurate identification is crucial for pathologists to diagnose and treat such conditions. However, this can be a difficult task, and previous attempts to automate the identification of podocytes have not been entirely successful. To address this issue, this study proposes a novel approach that combines pathologists’ expertise with an automated classifier to enhance the identification of podocytopathies. The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing a convolutional neural network (CNN) based classifier. The results showed that our automated classifier achieved an impressive 90.9% f-score. When the pathologists used as an auxiliary tool to classify a second set of images, the medical group’s average performance increased significantly, from $91.4pm 12.5$ % to $96.1pm 2.9$ % of f-score. Fleiss’ kappa agreement among the pathologists also increased from 0.59 to 0.83. Conclusion: These findings suggest that automating this task can bring benefits for pathologists to correctly identify images of glomeruli with podocyte degeneration, leading to improved individual accuracy while raising agreement in diagnosing podocytopathies. This approach could have significant implications for the diagnosis and treatment of kidney diseases. Clinical impact: The approach presented in this study has the potential to enhance the accuracy of medical diagnoses for detecting podocyte abnormalities in glomeruli, which serve as biomarkers for various glomerular diseases.
{"title":"Enhancing Podocyte Degenerative Changes Identification With Pathologist Collaboration: Implications for Improved Diagnosis in Kidney Diseases","authors":"George Oliveira Barros;José Nathan Andrade Muller da Silva;Henrique Machado de Sousa Proença;Stanley Almeida Araújo;David Campos Wanderley;Luciano Rebouças de Oliveira;Washington Luis Conrado Dos-Santos;Angelo Amancio Duarte;Flavio de Barros Vidal","doi":"10.1109/JTEHM.2024.3455941","DOIUrl":"10.1109/JTEHM.2024.3455941","url":null,"abstract":"Podocyte degenerative changes are common in various kidney diseases, and their accurate identification is crucial for pathologists to diagnose and treat such conditions. However, this can be a difficult task, and previous attempts to automate the identification of podocytes have not been entirely successful. To address this issue, this study proposes a novel approach that combines pathologists’ expertise with an automated classifier to enhance the identification of podocytopathies. The study involved building a new dataset of renal glomeruli images, some with and others without podocyte degenerative changes, and developing a convolutional neural network (CNN) based classifier. The results showed that our automated classifier achieved an impressive 90.9% f-score. When the pathologists used as an auxiliary tool to classify a second set of images, the medical group’s average performance increased significantly, from \u0000<inline-formula> <tex-math>$91.4pm 12.5$ </tex-math></inline-formula>\u0000% to \u0000<inline-formula> <tex-math>$96.1pm 2.9$ </tex-math></inline-formula>\u0000% of f-score. Fleiss’ kappa agreement among the pathologists also increased from 0.59 to 0.83. Conclusion: These findings suggest that automating this task can bring benefits for pathologists to correctly identify images of glomeruli with podocyte degeneration, leading to improved individual accuracy while raising agreement in diagnosing podocytopathies. This approach could have significant implications for the diagnosis and treatment of kidney diseases. Clinical impact: The approach presented in this study has the potential to enhance the accuracy of medical diagnoses for detecting podocyte abnormalities in glomeruli, which serve as biomarkers for various glomerular diseases.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"635-642"},"PeriodicalIF":3.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181840","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 : 2024-09-03DOI: 10.1109/JTEHM.2024.3454077
Zhichuan Tang;Zhixuan Cui;Hang Wang;Pengcheng Liu;Xuan Xu;Keshuai Yang
Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.
{"title":"A 4-DOF Exosuit Using a Hybrid EEG-Based Control Approach for Upper-Limb Rehabilitation","authors":"Zhichuan Tang;Zhixuan Cui;Hang Wang;Pengcheng Liu;Xuan Xu;Keshuai Yang","doi":"10.1109/JTEHM.2024.3454077","DOIUrl":"https://doi.org/10.1109/JTEHM.2024.3454077","url":null,"abstract":"Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.","PeriodicalId":54255,"journal":{"name":"IEEE Journal of Translational Engineering in Health and Medicine-Jtehm","volume":"12 ","pages":"622-634"},"PeriodicalIF":3.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10663725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169698","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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