Pub Date : 2024-06-05DOI: 10.1109/OJEMB.2024.3410118
Yacine M'Rad;Caecilia Charbonnier;Marcelo Elias de Oliveira;Pauline Coralie Guillemin;Lindsey Alexandra Crowe;Thibaud Kössler;Pierre-Alexandre Poletti;Sana Boudabbous;Alexis Ricoeur;Rares Salomir;Orane Lorton
Purpose: Transducer positioning for liver ablation by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) is challenging due to the presence of air-filled organs or bones on the beam path. This paper presents a software tool developed to optimize the positioning of a HIFU transducer dedicated to abdominal thermal therapy, to maximize the treatment's efficiency while minimizing the near-field risk. Methods: A software tool was developed to determine the theoretical optimal position (TOP) of the transducer based on the minimization of a cost function using the particle swarm optimization (PSO). After an initialization phase and a manual segmentation of the abdomen of 5 pigs, the program randomly generates particles with 2 degrees of freedom and iteratively minimizes the cost function of the particles considering 3 parameters weighted according to their criticality. New particles are generated around the best position obtained at the previous step and the process is repeated until the optimal position of the transducer is reached. MR imaging data from in vivo HIFU ablation in pig livers was used for ground truth comparison between the TOP and the experimental position (EP). Results: As compared to the manual EP, the rotation difference with the TOP was on average −3.1 ± 7.1° and the distance difference was on average −7.1 ± 5.4 mm. The computational time to suggest the TOP was 20s. The software tool is modulable and demonstrated consistency and robustness when repeating the calculation and changing the initial position of the transducer.
目的：磁共振引导下高强度聚焦超声（MRgHIFU）肝脏消融的换能器定位具有挑战性，因为在射束路径上存在充满空气的器官或骨骼。本文介绍了一种软件工具，用于优化腹部热疗专用 HIFU 传感器的定位，以最大限度地提高治疗效率，同时最大限度地降低近场风险。方法：利用粒子群优化（PSO）技术，在最小化成本函数的基础上，开发了一种软件工具来确定换能器的理论最佳位置（TOP）。在初始化阶段和手动分割 5 头猪的腹部后，程序随机生成具有 2 个自由度的粒子，并根据粒子的临界值加权考虑 3 个参数，反复最小化粒子的成本函数。新粒子围绕上一步获得的最佳位置生成，该过程重复进行，直到达到换能器的最佳位置。猪肝脏活体 HIFU 消融的磁共振成像数据被用来比较 TOP 位置和实验位置 (EP) 之间的基本真实情况。结果：与手动 EP 相比，TOP 的旋转差平均为 -3.1 ± 7.1°，距离差平均为 -7.1 ± 5.4 mm。建议 TOP 的计算时间为 20 秒。该软件工具是可修改的，在重复计算和改变传感器初始位置时表现出一致性和稳健性。
{"title":"Computer-Aided Intra-Operatory Positioning of an MRgHIFU Applicator Dedicated to Abdominal Thermal Therapy Using Particle Swarm Optimization","authors":"Yacine M'Rad;Caecilia Charbonnier;Marcelo Elias de Oliveira;Pauline Coralie Guillemin;Lindsey Alexandra Crowe;Thibaud Kössler;Pierre-Alexandre Poletti;Sana Boudabbous;Alexis Ricoeur;Rares Salomir;Orane Lorton","doi":"10.1109/OJEMB.2024.3410118","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3410118","url":null,"abstract":"Purpose: Transducer positioning for liver ablation by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) is challenging due to the presence of air-filled organs or bones on the beam path. This paper presents a software tool developed to optimize the positioning of a HIFU transducer dedicated to abdominal thermal therapy, to maximize the treatment's efficiency while minimizing the near-field risk. Methods: A software tool was developed to determine the theoretical optimal position (TOP) of the transducer based on the minimization of a cost function using the particle swarm optimization (PSO). After an initialization phase and a manual segmentation of the abdomen of 5 pigs, the program randomly generates particles with 2 degrees of freedom and iteratively minimizes the cost function of the particles considering 3 parameters weighted according to their criticality. New particles are generated around the best position obtained at the previous step and the process is repeated until the optimal position of the transducer is reached. MR imaging data from \u0000<italic>in vivo</i>\u0000 HIFU ablation in pig livers was used for ground truth comparison between the TOP and the experimental position (EP). Results: As compared to the manual EP, the rotation difference with the TOP was on average −3.1 ± 7.1° and the distance difference was on average −7.1 ± 5.4 mm. The computational time to suggest the TOP was 20s. The software tool is modulable and demonstrated consistency and robustness when repeating the calculation and changing the initial position of the transducer.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"524-533"},"PeriodicalIF":2.7,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10549770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-04DOI: 10.1109/OJEMB.2024.3408092
Robert D. Butterfield;Nathaniel M. Sims
Goal: An insulin pump's failure to deliver insulin in the right amount at the right time is a preventable cause of hospitalization. We evaluated key performance metrics of a novel insulin pump that prevents “silent insulin non-delivery” caused by blockage, delivery of air and site leakage. This is accomplished via an acoustic sensor that measures the volume of insulin delivered with each pulse in real-time. Methods: We tested long and short-term flow accuracy, occlusion-detection time and pressure, and air management of the new device (ND) versus 3 U.S. commercial insulin pumps (CIPs) using standardized methods. Results: The ND outperformed CIPs on long-term basal flow rate error. Occlusion detection was 5 to 22.5 times faster depending on the basal rate and resulted in significantly lower (2 to 5x) pressures at time of occlusion. With air included in the drug reservoir, the tested CIPs can infuse air without detection, while the ND prevented air delivery without interruption. Conclusions: Bench tests of the ND versus 3 commercially available pumps showed improved occlusion detection and air management without flow performance tradeoffs. Additionally, the lower delivery pressure measured at time of occlusion suggests a substantially lower potential for site leakage at both basal and bolus rates. These enhancements combine to decrease the likelihood of silent insulin non-delivery.
{"title":"Performance of a Continuous Subcutaneous Insulin Infusion (CSII) Pump With Acoustic Volume and Flow Sensing in Simulated High-Consequence Situations","authors":"Robert D. Butterfield;Nathaniel M. Sims","doi":"10.1109/OJEMB.2024.3408092","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3408092","url":null,"abstract":"<italic>Goal:</i>\u0000 An insulin pump's failure to deliver insulin in the right amount at the right time is a preventable cause of hospitalization. We evaluated key performance metrics of a novel insulin pump that prevents “silent insulin non-delivery” caused by blockage, delivery of air and site leakage. This is accomplished via an acoustic sensor that measures the volume of insulin delivered with each pulse in real-time. \u0000<italic>Methods:</i>\u0000 We tested long and short-term flow accuracy, occlusion-detection time and pressure, and air management of the new device (ND) versus 3 U.S. commercial insulin pumps (CIPs) using standardized methods. \u0000<italic>Results:</i>\u0000 The ND outperformed CIPs on long-term basal flow rate error. Occlusion detection was 5 to 22.5 times faster depending on the basal rate and resulted in significantly lower (2 to 5x) pressures at time of occlusion. With air included in the drug reservoir, the tested CIPs can infuse air without detection, while the ND prevented air delivery without interruption. \u0000<italic>Conclusions:</i>\u0000 Bench tests of the ND versus 3 commercially available pumps showed improved occlusion detection and air management without flow performance tradeoffs. Additionally, the lower delivery pressure measured at time of occlusion suggests a substantially lower potential for site leakage at both basal and bolus rates. These enhancements combine to decrease the likelihood of silent insulin non-delivery.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"593-599"},"PeriodicalIF":2.7,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547403","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-04DOI: 10.1109/OJEMB.2024.3409086
Bjoern M. Eskofier;Martin Vossiek
{"title":"Guest Editorial Introduction to the Special Section on Invisible Sensing: Radar-Based Biomonitoring","authors":"Bjoern M. Eskofier;Martin Vossiek","doi":"10.1109/OJEMB.2024.3409086","DOIUrl":"10.1109/OJEMB.2024.3409086","url":null,"abstract":"","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"678-679"},"PeriodicalIF":2.7,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.1109/OJEMB.2024.3408454
F. S. Rahman;Z. Yousuf;F. Castelán;M. Martínez-Gómez;Y. M. Akay;P. Zimmern;M. Akay;M. I. Romero-Ortega
Objective: Stress urinary incontinence (SUI) affects a third of the female population and is characterized by involuntary urine leakage during abdominal efforts such as sneezing, laughing, or coughing. Acute neuromodulation of the bulbospongiosus nerve (BsN) was shown to increase bladder efficiency in aged and multiparous rabbits. This study investigates the efficacy of sub-chronic BsN neuromodulation in alleviating SUI-like deficits in mature multiparous rabbits, characterized by increased urine leakage and reduced leak point pressure . Results: Using the voiding spot assay, we observed a 40% reduction in urine leakage events after 30 days of BsN stimulation, which correlated with a 60% increase in daily micturition volume, a 10-fold increase in voided volume, and improvements in voiding efficiency and leak point pressure compared to negative control animals. Conclusion: In multiparous rabbits, BsN neuromodulation improves important SUI-like metrics including bladder capacity and urethral closure, supporting the use of this bioelectronic modality as treatment for SUI.
目的：压力性尿失禁（SUI）影响着三分之一的女性人口，其特征是在打喷嚏、大笑或咳嗽等腹部用力时不自主漏尿。研究表明，对球海绵体神经（BsN）进行急性神经调节可提高老年和多胎兔子的膀胱效率。本研究调查了亚慢性 BsN 神经调节对缓解成熟多胎兔 SUI 类缺陷的疗效，这些缺陷的特点是漏尿增加和漏点压力降低。研究结果与阴性对照组相比，使用排尿点测定法，我们观察到 BsN 刺激 30 天后，漏尿事件减少了 40%，这与每日排尿量增加 60%、排尿量增加 10 倍以及排尿效率和漏尿点压力提高有关。结论在多胎兔中，BsN 神经调节改善了类似 SUI 的重要指标，包括膀胱容量和尿道闭合，支持使用这种生物电子方式治疗 SUI。
{"title":"Neuromodulation Improves Stress Urinary Incontinence-Like Deficits in Female Rabbits","authors":"F. S. Rahman;Z. Yousuf;F. Castelán;M. Martínez-Gómez;Y. M. Akay;P. Zimmern;M. Akay;M. I. Romero-Ortega","doi":"10.1109/OJEMB.2024.3408454","DOIUrl":"10.1109/OJEMB.2024.3408454","url":null,"abstract":"<italic>Objective:</i>\u0000 Stress urinary incontinence (SUI) affects a third of the female population and is characterized by involuntary urine leakage during abdominal efforts such as sneezing, laughing, or coughing. Acute neuromodulation of the bulbospongiosus nerve (BsN) was shown to increase bladder efficiency in aged and multiparous rabbits. This study investigates the efficacy of sub-chronic BsN neuromodulation in alleviating SUI-like deficits in mature multiparous rabbits, characterized by increased urine leakage and reduced leak point pressure\u0000<italic>. Results:</i>\u0000 Using the voiding spot assay, we observed a 40% reduction in urine leakage events after 30 days of BsN stimulation, which correlated with a 60% increase in daily micturition volume, a 10-fold increase in voided volume, and improvements in voiding efficiency and leak point pressure compared to negative control animals. \u0000<italic>Conclusion:</i>\u0000 In multiparous rabbits, BsN neuromodulation improves important SUI-like metrics including bladder capacity and urethral closure, supporting the use of this bioelectronic modality as treatment for SUI.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"10-19"},"PeriodicalIF":2.7,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10545601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.1109/OJEMB.2024.3408078
Sevgi Z. Gurbuz;Mohammad Mahbubur Rahman;Zahra Bassiri;Dario Martelli
Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults’ mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations – in particular, the micro-Doppler signature and skeletal point estimates – is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.
{"title":"Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment","authors":"Sevgi Z. Gurbuz;Mohammad Mahbubur Rahman;Zahra Bassiri;Dario Martelli","doi":"10.1109/OJEMB.2024.3408078","DOIUrl":"10.1109/OJEMB.2024.3408078","url":null,"abstract":"Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults’ mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations – in particular, the micro-Doppler signature and skeletal point estimates – is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"735-749"},"PeriodicalIF":2.7,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10546280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: A biological system's internal morphological structure or function can be changed as a result of the mechanical effect of focused ultrasound. Pulsed low-intensity focused ultrasound (LIFU) has mechanical effects that might induce follicle development with less damage to ovarian tissue. The potential development of LIFU as a non-invasive method for the treatment of female infertility is being considered, and this study sought to explore and confirm that LIFU can activate ovarian follicles. Results: We found a 50% increase in ovarian weight and in the number of mature follicles on the ultrasound-stimulated side with pulsed LIFU and intraperitoneal injection of 10 IU PMSG in 10-day-old rats. After ultrasound stimulation, the PCOS-like rats had a decrease in androgen levels, restoration of regular estrous cycle and increase in the number of mature follicles and corpora lutea, and the ratio of M1 and M2 type macrophages was altered in antral follicles of PCOS-like rats, consequently promoting further development and maturation of antral follicles. Conclusion: LIFU treatment could trigger actin changes in ovarian cells, which might disrupt the Hippo signal pathway to promote follicle formation, and the mechanical impact on the ovaries of PCOS-like rats improved antral follicle development.
{"title":"Pulsed Low-Intensity Focused Ultrasound (LIFU) Activation of Ovarian Follicles","authors":"Yan Xiao;Lixia Yang;Yicong Wang;Yu Wang;Yuning Chen;Wenhan Lu;Zhenle Pei;Ruonan Zhang;Yao Ye;Xiaowei Ji;Suying Liu;Xi Dong;Yonghua Xu;Yi Feng","doi":"10.1109/OJEMB.2024.3391939","DOIUrl":"10.1109/OJEMB.2024.3391939","url":null,"abstract":"<italic>Objective:</i>\u0000 A biological system's internal morphological structure or function can be changed as a result of the mechanical effect of focused ultrasound. Pulsed low-intensity focused ultrasound (LIFU) has mechanical effects that might induce follicle development with less damage to ovarian tissue. The potential development of LIFU as a non-invasive method for the treatment of female infertility is being considered, and this study sought to explore and confirm that LIFU can activate ovarian follicles. \u0000<italic>Results:</i>\u0000 We found a 50% increase in ovarian weight and in the number of mature follicles on the ultrasound-stimulated side with pulsed LIFU and intraperitoneal injection of 10 IU PMSG in 10-day-old rats. After ultrasound stimulation, the PCOS-like rats had a decrease in androgen levels, restoration of regular estrous cycle and increase in the number of mature follicles and corpora lutea, and the ratio of M1 and M2 type macrophages was altered in antral follicles of PCOS-like rats, consequently promoting further development and maturation of antral follicles. \u0000<italic>Conclusion:</i>\u0000 LIFU treatment could trigger actin changes in ovarian cells, which might disrupt the Hippo signal pathway to promote follicle formation, and the mechanical impact on the ovaries of PCOS-like rats improved antral follicle development.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"316-329"},"PeriodicalIF":5.8,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508955","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140803921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal: Parkinson's disease (PD) can lead to gait impairment and Freezing of Gait (FoG). Recent advances in cueing technologies have enhanced mobility in PD patients. While sensor technology and machine learning offer real-time detection for on-demand cueing, existing systems are limited by the usage of smartphones between the sensor(s) and cueing device(s) for data processing. By avoiding this we aim at improving usability, robustness, and detection delay. Methods: We present a new technical solution, that runs detection and cueing algorithms directly on the sensing and cueing devices, bypassing the smartphone. This solution relies on edge computing on the devices' hardware. The wearable system consists of a single inertial sensor to control a stimulator and enables machine-learning-based FoG detection by classifying foot motion phases as either normal or FoG-affected. We demonstrate the system's functionality and safety during on-demand gait-synchronous electrical cueing in two patients, performing freezing of gait assessments. As references, motion phases and FoG episodes have been video-annotated. Results: The analysis confirms adequate gait phase and FoG detection performance. The mobility assistant detected foot motions with a rate above 94 % and classified them with an accuracy of 84 % into normal or FoG-affected. The FoG detection delay is mainly defined by the foot-motion duration, which is below the delay in existing sliding-window approaches. Conclusions: Direct computing on the sensor and cueing devices ensures robust detection of FoG-affected motions for on demand cueing synchronized with the gait. The proposed solution can be easily adopted to other sensor and cueing modalities.
目标：帕金森病（PD）可导致步态障碍和步态冻结（FoG）。提示技术的最新进展提高了帕金森病患者的行动能力。虽然传感器技术和机器学习为按需提示提供了实时检测功能，但现有系统却受限于在传感器和提示设备之间使用智能手机进行数据处理。通过避免这种情况，我们的目标是提高可用性、鲁棒性和检测延迟。方法：我们提出了一种新的技术解决方案，绕过智能手机，直接在传感和提示设备上运行检测和提示算法。该解决方案依赖于设备硬件上的边缘计算。该可穿戴系统由一个惯性传感器组成，用于控制一个刺激器，并通过将脚部运动阶段分类为正常或受 FoG 影响，实现基于机器学习的 FoG 检测。我们在两名患者身上演示了该系统的功能性和安全性，在按需进行步态同步电提示的过程中，对步态进行了冻结评估。作为参考，我们对运动阶段和 FoG 事件进行了视频标注。结果分析证实，步态相位和 FoG 检测性能良好。助行器对足部运动的检测率超过 94%，对正常或受 FoG 影响足部运动的分类准确率为 84%。FoG 检测延迟主要由脚部运动持续时间决定，低于现有滑动窗口方法的延迟。结论传感器和提示设备上的直接计算可确保对受 FoG 影响的运动进行稳健检测，从而实现与步态同步的按需提示。所提出的解决方案可轻松应用于其他传感器和提示模式。
{"title":"On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study","authors":"Ardit Dvorani;Constantin Wiesener;Christina Salchow-Hömmen;Magdalena Jochner;Lotta Spieker;Matej Skrobot;Hanno Voigt;Andrea Kühn;Nikolaus Wenger;Thomas Schauer","doi":"10.1109/OJEMB.2024.3390562","DOIUrl":"10.1109/OJEMB.2024.3390562","url":null,"abstract":"<italic>Goal:</i>\u0000 Parkinson's disease (PD) can lead to gait impairment and Freezing of Gait (FoG). Recent advances in cueing technologies have enhanced mobility in PD patients. While sensor technology and machine learning offer real-time detection for on-demand cueing, existing systems are limited by the usage of smartphones between the sensor(s) and cueing device(s) for data processing. By avoiding this we aim at improving usability, robustness, and detection delay. \u0000<italic>Methods:</i>\u0000 We present a new technical solution, that runs detection and cueing algorithms directly on the sensing and cueing devices, bypassing the smartphone. This solution relies on edge computing on the devices' hardware. The wearable system consists of a single inertial sensor to control a stimulator and enables machine-learning-based FoG detection by classifying foot motion phases as either normal or FoG-affected. We demonstrate the system's functionality and safety during on-demand gait-synchronous electrical cueing in two patients, performing freezing of gait assessments. As references, motion phases and FoG episodes have been video-annotated. \u0000<italic>Results:</i>\u0000 The analysis confirms adequate gait phase and FoG detection performance. The mobility assistant detected foot motions with a rate above 94 % and classified them with an accuracy of 84 % into normal or FoG-affected. The FoG detection delay is mainly defined by the foot-motion duration, which is below the delay in existing sliding-window approaches. \u0000<italic>Conclusions:</i>\u0000 Direct computing on the sensor and cueing devices ensures robust detection of FoG-affected motions for on demand cueing synchronized with the gait. The proposed solution can be easily adopted to other sensor and cueing modalities.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"306-315"},"PeriodicalIF":5.8,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140625436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-05DOI: 10.1109/OJEMB.2024.3377461
Youngbin Kim;Kunlun Wang;Roberta I. Lock;Trevor R. Nash;Sharon Fleischer;Bryan Z. Wang;Barry M. Fine;Gordana Vunjak-Novakovic
Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.
{"title":"BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs","authors":"Youngbin Kim;Kunlun Wang;Roberta I. Lock;Trevor R. Nash;Sharon Fleischer;Bryan Z. Wang;Barry M. Fine;Gordana Vunjak-Novakovic","doi":"10.1109/OJEMB.2024.3377461","DOIUrl":"https://doi.org/10.1109/OJEMB.2024.3377461","url":null,"abstract":"<italic>Goal:</i>\u0000 Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. \u0000<italic>Methods:</i>\u0000 We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). \u0000<italic>Results:</i>\u0000 Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. \u0000<italic>Conclusions:</i>\u0000 We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"238-249"},"PeriodicalIF":5.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10490213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-03DOI: 10.1109/OJEMB.2024.3384488
Youngro Lee;Jongae Park;Sungjoon Park;Jongmo Seo;Hae-Young Lee
Recent advancements in smartwatch technology have introduced photoplethysmography (PPG)-based blood pressure (BP) estimation, enabling convenient and continuous monitoring of BP. However, concerns about accuracy and validation for clinical use persist. This study uses real-world data from a Samsung Galaxy Watch campaign to assess smartwatch-based BP measurements. The approach examines calibration stability by comparing average systolic BP (SBP) before and after calibration, identifying factors affecting stability through regression analysis. User-level strategies are suggested to mitigate calibration instability and emphasize guideline adherence. Notably, calibration instability is found to decrease during night-time measurements and when averaging multiple readings in the same time frame. Guideline adherence is vital, particularly for the elderly, females, and individuals with hypertension. The research enhances measurement reliability through extensive datasets, shedding light on calibration stability.
{"title":"Stability of Watch-Based Blood Pressure Measurements Analyzed by Pre-Post Calibration Differences","authors":"Youngro Lee;Jongae Park;Sungjoon Park;Jongmo Seo;Hae-Young Lee","doi":"10.1109/OJEMB.2024.3384488","DOIUrl":"10.1109/OJEMB.2024.3384488","url":null,"abstract":"Recent advancements in smartwatch technology have introduced photoplethysmography (PPG)-based blood pressure (BP) estimation, enabling convenient and continuous monitoring of BP. However, concerns about accuracy and validation for clinical use persist. This study uses real-world data from a Samsung Galaxy Watch campaign to assess smartwatch-based BP measurements. The approach examines calibration stability by comparing average systolic BP (SBP) before and after calibration, identifying factors affecting stability through regression analysis. User-level strategies are suggested to mitigate calibration instability and emphasize guideline adherence. Notably, calibration instability is found to decrease during night-time measurements and when averaging multiple readings in the same time frame. Guideline adherence is vital, particularly for the elderly, females, and individuals with hypertension. The research enhances measurement reliability through extensive datasets, shedding light on calibration stability.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"828-836"},"PeriodicalIF":2.7,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10490153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Goal : To evaluate the usability of different technologies designed for a remote assessment of knee osteoarthritis. Methods: We recruited eleven patients affected by mild or moderate knee osteoarthritis, eleven caregivers, and eleven clinicians to assess the following technologies: a wristband for monitoring physical activity, an examination chair for measuring leg extension, a thermal camera for acquiring skin thermographic data, a force balance for measuring center of pressure, an ultrasound imaging system for remote echographic acquisition, a mobile app, and a clinical portal software. Specific questionnaires scoring usability were filled out by patients, caregivers and clinicians. Results: The questionnaires highlighted a good level of usability and user-friendliness for all the technologies, obtaining an average score of 8.7 provided by the patients, 8.8 by the caregivers, and 8.5 by the clinicians, on a scale ranging from 0 to 10. Such average scores were calculated by putting together the scores obtained for the single technologies under evaluation and averaging them. Conclusions: This study demonstrates a high level of acceptability for the tested portable technologies designed for a potentially remote and frequent assessment of knee osteoarthritis.
{"title":"Usability Assessment of Technologies for Remote Monitoring of Knee Osteoarthritis","authors":"Andrea Cafarelli;Angela Sorriento;Giorgia Marola;Denise Amram;Fabien Rabusseau;Hervé Locteau;Paolo Cabras;Erik Dumont;Sam Nakhaei;Ake Jernberger;Pär Bergsten;Paolo Spinnato;Alessandro Russo;Leonardo Ricotti","doi":"10.1109/OJEMB.2024.3407961","DOIUrl":"10.1109/OJEMB.2024.3407961","url":null,"abstract":"<italic>Goal</i>\u0000: To evaluate the usability of different technologies designed for a remote assessment of knee osteoarthritis. \u0000<italic>Methods:</i>\u0000 We recruited eleven patients affected by mild or moderate knee osteoarthritis, eleven caregivers, and eleven clinicians to assess the following technologies: a wristband for monitoring physical activity, an examination chair for measuring leg extension, a thermal camera for acquiring skin thermographic data, a force balance for measuring center of pressure, an ultrasound imaging system for remote echographic acquisition, a mobile app, and a clinical portal software. Specific questionnaires scoring usability were filled out by patients, caregivers and clinicians. \u0000<italic>Results:</i>\u0000 The questionnaires highlighted a good level of usability and user-friendliness for all the technologies, obtaining an average score of 8.7 provided by the patients, 8.8 by the caregivers, and 8.5 by the clinicians, on a scale ranging from 0 to 10. Such average scores were calculated by putting together the scores obtained for the single technologies under evaluation and averaging them. \u0000<italic>Conclusions:</i>\u0000 This study demonstrates a high level of acceptability for the tested portable technologies designed for a potentially remote and frequent assessment of knee osteoarthritis.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"476-484"},"PeriodicalIF":5.8,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10543164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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