Pub Date : 2024-09-20DOI: 10.1109/TBME.2024.3464104
Hamidreza Asemani;Jannick P. Rolland;Kevin J. Parker
Objective: In shear wave elastography (SWE), the aim is to measure the velocity of shear waves, however unwanted compression waves and bulk tissue motion pose challenges in evaluating tissue stiffness. Conventional approaches often struggle to discriminate between shear and compression waves, leading to inaccurate shear wave speed (SWS) estimation. In this study, we propose a novel approach known as the integrated difference autocorrelation (IDA) estimator to accurately estimate reverberant SWS in the presence of compression waves and noise. Methods: The IDA estimator, unlike conventional techniques, computes the subtraction of velocity between neighboring particles, effectively minimizing the impact of long wavelength compression waves and other wide-area movements such as those caused by respiration. We evaluated the effectiveness of IDA by: (1) using k-Wave simulations of a branching cylinder in a soft background, (2) using ultrasound elastography on a breast phantom, (3) using ultrasound elastography in the human liver-kidney region, and (4) using magnetic resonance elastography (MRE) on a brain phantom. Results: By applying IDA to unfiltered contaminated wave fields of simulation and elastography experiments, the estimated SWSs are in good agreement with the ground truth values (i.e., less than 2% error for the simulation, 9% error for ultrasound elastography of the breast phantom and 19% error for MRE). Conclusion: Our results demonstrate that IDA accurately estimates SWS, revealing the existence of a lesion, even in the presence of strong compression waves. Significance: IDA exhibits consistency in SWS estimation across different modalities and excitation scenarios, highlighting its robustness and potential clinical utility.
目的：在共振波弹性成像（SWE）中，目的是测量剪切波的速度，然而不需要的压缩波和组织块运动给评估组织硬度带来了挑战。传统方法往往难以区分剪切波和压缩波，导致剪切波速度（SWS）估计不准确。在这项研究中，我们提出了一种称为集成差分自相关（IDA）估计器的新方法，用于在存在压缩波和噪声的情况下准确估计混响的 SWS：与传统技术不同，IDA 估计器计算相邻颗粒之间的速度减法，从而有效地减少了长波长压缩波和其他大范围运动（如呼吸引起的运动）的影响。我们通过以下方法评估了 IDA 的有效性：(1) 使用 k 波模拟软背景中的分支圆柱体，(2) 在乳房模型上使用超声弹性成像，(3) 在人体肝肾区域使用超声弹性成像，以及 (4) 在大脑模型上使用磁共振弹性成像 (MRE)：将 IDA 应用于模拟和弹性成像实验的未过滤污染波场，估算出的 SWS 与地面真实值非常吻合（即模拟误差小于 2%，乳腺模型超声弹性成像误差为 9%，MRE 误差为 19%）：我们的研究结果表明，即使存在强压缩波，IDA 也能准确估计 SWS，揭示病变的存在：意义：IDA 在不同模式和激发情况下对 SWS 的估算具有一致性，突出了其稳健性和潜在的临床实用性。
{"title":"Integrated Difference Autocorrelation: A Novel Approach to Estimate Shear Wave Speed in the Presence of Compression Waves","authors":"Hamidreza Asemani;Jannick P. Rolland;Kevin J. Parker","doi":"10.1109/TBME.2024.3464104","DOIUrl":"10.1109/TBME.2024.3464104","url":null,"abstract":"<italic>Objective:</i> In shear wave elastography (SWE), the aim is to measure the velocity of shear waves, however unwanted compression waves and bulk tissue motion pose challenges in evaluating tissue stiffness. Conventional approaches often struggle to discriminate between shear and compression waves, leading to inaccurate shear wave speed (SWS) estimation. In this study, we propose a novel approach known as the integrated difference autocorrelation (IDA) estimator to accurately estimate reverberant SWS in the presence of compression waves and noise. <italic>Methods:</i> The IDA estimator, unlike conventional techniques, computes the subtraction of velocity between neighboring particles, effectively minimizing the impact of long wavelength compression waves and other wide-area movements such as those caused by respiration. We evaluated the effectiveness of IDA by: (1) using k-Wave simulations of a branching cylinder in a soft background, (2) using ultrasound elastography on a breast phantom, (3) using ultrasound elastography in the human liver-kidney region, and (4) using magnetic resonance elastography (MRE) on a brain phantom. <italic>Results:</i> By applying IDA to unfiltered contaminated wave fields of simulation and elastography experiments, the estimated SWSs are in good agreement with the ground truth values (i.e., less than 2% error for the simulation, 9% error for ultrasound elastography of the breast phantom and 19% error for MRE). <italic>Conclusion:</i> Our results demonstrate that IDA accurately estimates SWS, revealing the existence of a lesion, even in the presence of strong compression waves. <italic>Significance:</i> IDA exhibits consistency in SWS estimation across different modalities and excitation scenarios, highlighting its robustness and potential clinical utility.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"586-594"},"PeriodicalIF":4.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/TBME.2024.3465373
B. Song;M. Paolieri;H. E. Stewart;L. Golubchik;J. L. McNitt-Gray;V. Misra;D. Shah
Objective: Our aim is to determine if data collected with inertial measurement units (IMUs) during steady-state running could be used to estimate ground reaction forces (GRFs) and to derive biomechanical variables (e.g., contact time, impulse, change in velocity) using lightweight machine-learning approaches. In contrast, state-of-the-art estimation using LSTMs suffers from prohibitive inference times on edge devices, requires expensive training and hyperparameter optimization, and results in black box models. Methods: We proposed a novel lightweight solution, SVD Embedding Regression (SER), using linear regression between SVD embeddings of IMU data and GRF data. We also compared lightweight solutions including SER and k-Nearest-Neighbors (KNN) regression with state-of-the-art LSTMs. Results: We performed extensive experiments to evaluate these techniques under multiple scenarios and combinations of IMU signals and quantified estimation errors for predicting GRFs and biomechanical variables. We did this using training data from different athletes, from the same athlete, or both, and we explored the use of acceleration and angular velocity data from sensors at different locations (sacrum and shanks). Conclusion: Our results illustrated that lightweight solutions such as SER and KNN can be similarly accurate or more accurate than LSTMs. The use of personal data reduced estimation errors of all methods, particularly for most biomechanical variables (as compared to GRFs); moreover, this gain was more pronounced in the lightweight methods. Significance: The study of GRFs is used to characterize the mechanical loading experienced by individuals in movements such as running, which is clinically applicable to identify athletes at risk for stress-related injuries.
{"title":"Estimating Ground Reaction Forces From Inertial Sensors","authors":"B. Song;M. Paolieri;H. E. Stewart;L. Golubchik;J. L. McNitt-Gray;V. Misra;D. Shah","doi":"10.1109/TBME.2024.3465373","DOIUrl":"10.1109/TBME.2024.3465373","url":null,"abstract":"<italic>Objective:</i> Our aim is to determine if data collected with inertial measurement units (IMUs) during steady-state running could be used to estimate ground reaction forces (GRFs) and to derive biomechanical variables (e.g., contact time, impulse, change in velocity) using lightweight machine-learning approaches. In contrast, state-of-the-art estimation using LSTMs suffers from prohibitive inference times on edge devices, requires expensive training and hyperparameter optimization, and results in black box models. <italic>Methods:</i> We proposed a novel lightweight solution, SVD Embedding Regression (SER), using linear regression between SVD embeddings of IMU data and GRF data. We also compared lightweight solutions including SER and k-Nearest-Neighbors (KNN) regression with state-of-the-art LSTMs. <italic>Results:</i> We performed extensive experiments to evaluate these techniques under multiple scenarios and combinations of IMU signals and quantified estimation errors for predicting GRFs and biomechanical variables. We did this using training data from different athletes, from the same athlete, or both, and we explored the use of acceleration and angular velocity data from sensors at different locations (sacrum and shanks). <italic>Conclusion:</i> Our results illustrated that lightweight solutions such as SER and KNN can be similarly accurate or more accurate than LSTMs. The use of personal data reduced estimation errors of all methods, particularly for most biomechanical variables (as compared to GRFs); moreover, this gain was more pronounced in the lightweight methods. <italic>Significance:</i> The study of GRFs is used to characterize the mechanical loading experienced by individuals in movements such as running, which is clinically applicable to identify athletes at risk for stress-related injuries.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"595-608"},"PeriodicalIF":4.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capturing numerous fragmented images and an inability to precisely locate and examine the region of interest (ROI) as traditional endoscopy can. Addressing this issue, image stitching around the ROI can be employed to aid in the diagnosis of gastrointestinal (GI) tract conditions. However, MCCE images possess unique characteristics, such as weak texture, close-up shooting, and large angle rotation, presenting challenges to current image-matching methods. In this context, a method named S2P-Matching is proposed for self-supervised patch-based matching in MCCE image stitching. The method involves augmenting the raw data by simulating the capsule endoscopic camera's behavior around the GI tract's ROI. Subsequently, an improved contrast learning encoder is utilized to extract local features, represented as deep feature descriptors. This encoder comprises two branches that extract distinct scale features, which are combined over the channel without manual labeling. The data-driven descriptors are then input into a Transformer model to obtain patch-level matches by learning the globally consented matching priors in the pseudo-ground-truth match pairs. Finally, the patch-level matching is refined and filtered to the pixel-level. The experimental results on real-world MCCE images demonstrate that S2P-Matching provides enhanced accuracy in addressing challenging issues in the GI tract environment with image parallax. The performance improvement can reach up to 203 and 55.8% in terms of NCM (Number of Correct Matches) and SR (Success Rate), respectively. This approach is expected to facilitate the wide adoption of MCCE-based gastrointestinal screening.
{"title":"S2P-Matching: Self-Supervised Patch-Based Matching Using Transformer for Capsule Endoscopic Images Stitching","authors":"Feng Lu;Dao Zhou;Haoyang Chen;Shuai Liu;Xianliang Ling;Lei Zhu;Tingting Gong;Bin Sheng;Xiaofei Liao;Hai Jin;Ping Li;David Dagan Feng","doi":"10.1109/TBME.2024.3462502","DOIUrl":"10.1109/TBME.2024.3462502","url":null,"abstract":"The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capturing numerous fragmented images and an inability to precisely locate and examine the region of interest (ROI) as traditional endoscopy can. Addressing this issue, image stitching around the ROI can be employed to aid in the diagnosis of gastrointestinal (GI) tract conditions. However, MCCE images possess unique characteristics, such as weak texture, close-up shooting, and large angle rotation, presenting challenges to current image-matching methods. In this context, a method named S2P-Matching is proposed for self-supervised patch-based matching in MCCE image stitching. The method involves augmenting the raw data by simulating the capsule endoscopic camera's behavior around the GI tract's ROI. Subsequently, an improved contrast learning encoder is utilized to extract local features, represented as deep feature descriptors. This encoder comprises two branches that extract distinct scale features, which are combined over the channel without manual labeling. The data-driven descriptors are then input into a Transformer model to obtain patch-level matches by learning the globally consented matching priors in the pseudo-ground-truth match pairs. Finally, the patch-level matching is refined and filtered to the pixel-level. The experimental results on real-world MCCE images demonstrate that S2P-Matching provides enhanced accuracy in addressing challenging issues in the GI tract environment with image parallax. The performance improvement can reach up to 203 and 55.8% in terms of NCM (Number of Correct Matches) and SR (Success Rate), respectively. This approach is expected to facilitate the wide adoption of MCCE-based gastrointestinal screening.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"540-551"},"PeriodicalIF":4.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/TBME.2024.3465654
Niall Holmes;James Leggett;Ryan M. Hill;Lukas Rier;Elena Boto;Holly Schofield;Tyler Hayward;Eliot Dawson;David Woolger;Vishal Shah;Samu Taulu;Matthew J. Brookes;Richard Bowtell
Wearable magnetoencephalography based on optically pumped magnetometers (OPM-MEG) offers non-invasive and high-fidelity measurement of human brain electrophysiology. The flexibility of OPM-MEG also means it can be deployed in participants of all ages and permits scanning during movement. However, the magnetic fields generated by neuronal currents – which form the basis of the OPM-MEG signal – are much smaller than environmental fields, and this means measurements are highly sensitive to interference. Further, OPMs have a low dynamic range, and should be operated in near-zero background field. Scanners must therefore be housed in specialised magnetically shielded rooms (MSRs), formed from multiple layers of shielding material. The MSR is a critical component, and current OPM-optimised shields are large (>3 m in height), heavy (>10,000 kg) and expensive (with up to 5 layers of material). This restricts the uptake of OPM-MEG technology. Here, we show that the application of the Maxwell filtering techniques signal space separation (SSS) and its spatiotemporal extension (tSSS) to OPM-MEG data can isolate small signals of interest measured in the presence of large interference. We compare phantom recordings and MEG data from a participant performing a motor task in a state-of-the-art 5-layer MSR, to similar data collected in a lightly shielded room: application of tSSS to data recorded in the lightly shielded room allowed accurate localisation of a dipole source in the phantom and neuronal sources in the brain. Our results point to future deployment of OPM-MEG in lighter, cheaper and easier-to-site MSRs which could catalyse widespread adoption of the technology.
{"title":"Wearable Magnetoencephalography in a Lightly Shielded Environment","authors":"Niall Holmes;James Leggett;Ryan M. Hill;Lukas Rier;Elena Boto;Holly Schofield;Tyler Hayward;Eliot Dawson;David Woolger;Vishal Shah;Samu Taulu;Matthew J. Brookes;Richard Bowtell","doi":"10.1109/TBME.2024.3465654","DOIUrl":"10.1109/TBME.2024.3465654","url":null,"abstract":"Wearable magnetoencephalography based on optically pumped magnetometers (OPM-MEG) offers non-invasive and high-fidelity measurement of human brain electrophysiology. The flexibility of OPM-MEG also means it can be deployed in participants of all ages and permits scanning during movement. However, the magnetic fields generated by neuronal currents – which form the basis of the OPM-MEG signal – are much smaller than environmental fields, and this means measurements are highly sensitive to interference. Further, OPMs have a low dynamic range, and should be operated in near-zero background field. Scanners must therefore be housed in specialised magnetically shielded rooms (MSRs), formed from multiple layers of shielding material. The MSR is a critical component, and current OPM-optimised shields are large (>3 m in height), heavy (>10,000 kg) and expensive (with up to 5 layers of material). This restricts the uptake of OPM-MEG technology. Here, we show that the application of the Maxwell filtering techniques signal space separation (SSS) and its spatiotemporal extension (tSSS) to OPM-MEG data can isolate small signals of interest measured in the presence of large interference. We compare phantom recordings and MEG data from a participant performing a motor task in a state-of-the-art 5-layer MSR, to similar data collected in a lightly shielded room: application of tSSS to data recorded in the lightly shielded room allowed accurate localisation of a dipole source in the phantom and neuronal sources in the brain. Our results point to future deployment of OPM-MEG in lighter, cheaper and easier-to-site MSRs which could catalyse widespread adoption of the technology.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"609-618"},"PeriodicalIF":4.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/TBME.2024.3443762
{"title":"IEEE Engineering in Medicine and Biology Society Information","authors":"","doi":"10.1109/TBME.2024.3443762","DOIUrl":"https://doi.org/10.1109/TBME.2024.3443762","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"71 10","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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/TBME.2024.3443764
{"title":"IEEE Transactions on Biomedical Engineering Information for Authors","authors":"","doi":"10.1109/TBME.2024.3443764","DOIUrl":"https://doi.org/10.1109/TBME.2024.3443764","url":null,"abstract":"","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"71 10","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Objective: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC). Methods: We employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of true-positive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN. Results: The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5$%$ of F-statistics across different LMNs. The prediction accuracy increased by up to 40$%$ across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96$%$/94$%$/96$%$ to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort. Conclusion: These domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery. Significance: DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery.
{"title":"Deep Learning-Based Tract Classification of Preoperative DWI Tractography Advances the Prediction of Short-Term Postoperative Language Improvement in Children With Drug-Resistant Epilepsy","authors":"Min-Hee Lee;Soumyanil Banerjee;Hiroshi Uda;Alanna Carlson;Ming Dong;Robert Rothermel;Csaba Juhász;Eishi Asano;Jeong-Won Jeong","doi":"10.1109/TBME.2024.3463481","DOIUrl":"10.1109/TBME.2024.3463481","url":null,"abstract":"<italic>Objective:</i> To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC). <italic>Methods:</i> We employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of true-positive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN. <italic>Results:</i> The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5<inline-formula><tex-math>$%$</tex-math></inline-formula> of F-statistics across different LMNs. The prediction accuracy increased by up to 40<inline-formula><tex-math>$%$</tex-math></inline-formula> across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96<inline-formula><tex-math>$%$</tex-math></inline-formula>/94<inline-formula><tex-math>$%$</tex-math></inline-formula>/96<inline-formula><tex-math>$%$</tex-math></inline-formula> to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort. <italic>Conclusion:</i> These domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery. <italic>Significance:</i> DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"565-576"},"PeriodicalIF":4.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1109/TBME.2024.3463873
Natali van Zijl;Abhirup Banerjee;Stephen John Payne
Objective: Dynamic cerebral autoregulation (dCA) refers to a collection of mechanisms that act to maintain steady state cerebral blood flow (CBF) near constant despite changes in arterial blood pressure (ABP), but which is known to become impaired in various cerebrovascular diseases. Currently, the mechanisms of dCA and how they are affected in different physiological conditions are poorly understood. The objective of this study was to disentangle the magnitudes and time scales of the myogenic and metabolic responses of dCA, in order to investigate how each mechanism is affected in impaired dCA. Methods: A physiological model of dCA was developed, where both the myogenic and metabolic responses were represented by a gain and time constant. Model parameters were optimized with pressure-flow impulse responses under normocapnic, thigh cuff, and hypercapnic conditions. The impulse responses were derived by applying transfer function analysis (TFA) to experimental recordings of ABP (Finapres), end-tidal CO2 (capnograph), and CBF velocity (transcranial doppler ultrasound in bilateral middle cerebral arteries). Results: The myogenic gain to time constant ratio was significantly smaller (p-values < 0.001 using both univariate and multivariate TFA), and the metabolic time constant was significantly larger (p-values < 0.001 using both univariate and multivariate TFA) in hypercapnia compared to normocapnia. Conclusion: Both the myogenic and metabolic responses were shown to be affected in impaired dCA, and the metabolic response was shown to be slowed down. Significance: This study contributes to the understanding of the complexities of dCA and how it is affected in different physiological conditions.
{"title":"Modeling the Mechanisms of Non-Neurogenic Dynamic Cerebral Autoregulation","authors":"Natali van Zijl;Abhirup Banerjee;Stephen John Payne","doi":"10.1109/TBME.2024.3463873","DOIUrl":"10.1109/TBME.2024.3463873","url":null,"abstract":"<italic>Objective</i>: Dynamic cerebral autoregulation (dCA) refers to a collection of mechanisms that act to maintain steady state cerebral blood flow (CBF) near constant despite changes in arterial blood pressure (ABP), but which is known to become impaired in various cerebrovascular diseases. Currently, the mechanisms of dCA and how they are affected in different physiological conditions are poorly understood. The objective of this study was to disentangle the magnitudes and time scales of the myogenic and metabolic responses of dCA, in order to investigate how each mechanism is affected in impaired dCA. <italic>Methods:</i> A physiological model of dCA was developed, where both the myogenic and metabolic responses were represented by a gain and time constant. Model parameters were optimized with pressure-flow impulse responses under normocapnic, thigh cuff, and hypercapnic conditions. The impulse responses were derived by applying transfer function analysis (TFA) to experimental recordings of ABP (Finapres), end-tidal CO<sub>2</sub> (capnograph), and CBF velocity (transcranial doppler ultrasound in bilateral middle cerebral arteries). <italic>Results:</i> The myogenic gain to time constant ratio was significantly smaller (p-values < 0.001 using both univariate and multivariate TFA), and the metabolic time constant was significantly larger (p-values < 0.001 using both univariate and multivariate TFA) in hypercapnia compared to normocapnia. <italic>Conclusion:</i> Both the myogenic and metabolic responses were shown to be affected in impaired dCA, and the metabolic response was shown to be slowed down. <italic>Significance:</i> This study contributes to the understanding of the complexities of dCA and how it is affected in different physiological conditions.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"577-585"},"PeriodicalIF":4.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1109/TBME.2024.3463199
Marco Mercuri;Giulia Sacco;Rainer Hornung;Huib Visser;Ilde Lorato;Stefano Pisa;Pierangelo Veltri;Guido Dolmans
In this work, we propose a signal processing technique for beam-steering radar architectures allowing concurrent two-dimensional (2-D) localization and vital signs monitoring of human subjects. We demonstrated it by using a single-input single-output (SISO) frequency-modulated continuous wave (FMCW) radar which integrates two frequency-scanning antennas (FSAs). This method is capable of isolating the Doppler signal generated by each single subject from the contributions of all the reflections in the monitored environment. This allows determining the number of individuals in the room and accurately measuring their vital signs parameters (respiration and heart rates) and 2-D positions (range and azimuth information). The spectral analysis, the data matrix generation and the signal processing technique are detailed and discussed. Experimental results demonstrated the feasibility of the proposed approach, showing the ability in determining the number of subjects present in the room, in accurately measuring and tracking over time their vital signs parameters, and in 2-D localization with errors within the limits of the radar range and angular resolutions. Practical applications arise for healthcare, Hospital 4.0, Internet of Medical Things (IoMT), ambient assisted living, smart buildings and through-wall sensing.
{"title":"Enhanced Technique for Accurate Localization and Life-Sign Detection of Human Subjects Using Beam-Steering Radar Architectures","authors":"Marco Mercuri;Giulia Sacco;Rainer Hornung;Huib Visser;Ilde Lorato;Stefano Pisa;Pierangelo Veltri;Guido Dolmans","doi":"10.1109/TBME.2024.3463199","DOIUrl":"10.1109/TBME.2024.3463199","url":null,"abstract":"In this work, we propose a signal processing technique for beam-steering radar architectures allowing concurrent two-dimensional (2-D) localization and vital signs monitoring of human subjects. We demonstrated it by using a single-input single-output (SISO) frequency-modulated continuous wave (FMCW) radar which integrates two frequency-scanning antennas (FSAs). This method is capable of isolating the Doppler signal generated by each single subject from the contributions of all the reflections in the monitored environment. This allows determining the number of individuals in the room and accurately measuring their vital signs parameters (respiration and heart rates) and 2-D positions (range and azimuth information). The spectral analysis, the data matrix generation and the signal processing technique are detailed and discussed. Experimental results demonstrated the feasibility of the proposed approach, showing the ability in determining the number of subjects present in the room, in accurately measuring and tracking over time their vital signs parameters, and in 2-D localization with errors within the limits of the radar range and angular resolutions. Practical applications arise for healthcare, Hospital 4.0, Internet of Medical Things (IoMT), ambient assisted living, smart buildings and through-wall sensing.","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"72 2","pages":"552-564"},"PeriodicalIF":4.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10683961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：