Pub Date : 2024-01-01DOI: 10.1016/j.bbe.2024.01.002
Xin Wen , Xing Guo , Shuihua Wang , Zhihai Lu , Yudong Zhang
The second-leading cause of death for women is breast cancer. Consequently, a precise early diagnosis is essential. With the rapid development of artificial intelligence, computer-aided diagnosis can efficiently assist radiologists in diagnosing breast problems. Mammography images, breast thermal images, and breast ultrasound images are the three ways to diagnose breast cancer. The paper will discuss some recent developments in machine learning and deep learning in three different breast cancer diagnosis methods. The three components of conventional machine learning methods are image preprocessing, segmentation, feature extraction, and image classification. Deep learning includes convolutional neural networks, transfer learning, and other methods. Additionally, the benefits and drawbacks of different methods are thoroughly contrasted. Finally, we also provide a summary of the challenges and potential futures for breast cancer diagnosis.
{"title":"Breast cancer diagnosis: A systematic review","authors":"Xin Wen , Xing Guo , Shuihua Wang , Zhihai Lu , Yudong Zhang","doi":"10.1016/j.bbe.2024.01.002","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.002","url":null,"abstract":"<div><p>The second-leading cause of death for women is breast cancer. Consequently, a precise early diagnosis is essential. With the rapid development of artificial intelligence, computer-aided diagnosis can efficiently assist radiologists in diagnosing breast problems. Mammography images, breast thermal images, and breast ultrasound images are the three ways to diagnose breast cancer. The paper will discuss some recent developments in machine learning and deep learning in three different breast cancer diagnosis methods. The three components of conventional machine learning methods are image preprocessing, segmentation, feature extraction, and image classification. Deep learning includes convolutional neural networks, transfer learning, and other methods. Additionally, the benefits and drawbacks of different methods are thoroughly contrasted. Finally, we also provide a summary of the challenges and potential futures for breast cancer diagnosis.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 119-148"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000020/pdfft?md5=91d91592ef5090eeae962ba35d05faee&pid=1-s2.0-S0208521624000020-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139488032","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 : 2024-01-01DOI: 10.1016/j.bbe.2024.01.001
Pawel Trajdos, Marek Kurzynski
The paper presents an original method for controlling a surface-electromyography-driven (sEMG) prosthesis. A context-dependent recognition system is proposed in which the same class of sEMG signals may have a different interpretation, depending on the context. This allowed the repertoire of performed movements to be increased. The proposed structure of the context-dependent recognition system includes unambiguously defined decision sequences covering the overall action of the prosthesis, i.e. the so-called boxes. Because the boxes are mutually isolated environments, each box has its own interpretation of the recognition result, as well as a separate local-recognition-task-focused classifier.
Due to the freedom to assign contextual meanings to classes of biosignals, the construction procedure of the classifier can be optimised in terms of the local classification quality in a given box or the classification quality of the entire system. In the paper, two optimisation problems are formulated, differing in the adopted constraints on optimisation variables, with the methods of solving the problems based on an exhaustive search and an evolutionary algorithm, being developed.
Experimental studies were conducted using signals from 1 able-bodied person with simulation of amputation and 10 volunteers with transradial amputations. The study compared the classical recognition system and the context-dependent system for various classifier models. An unusual testing strategy was adopted in the research, taking into account the specificity of the considered recognition task, with two original quality measures resulting from this scheme then being applied. The results obtained confirm the hypothesis that the application of the context-dependent classifier led to an improvement in classification quality.
{"title":"Application of context-dependent interpretation of biosignals recognition to control a bionic multifunctional hand prosthesis","authors":"Pawel Trajdos, Marek Kurzynski","doi":"10.1016/j.bbe.2024.01.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.001","url":null,"abstract":"<div><p>The paper presents an original method for controlling a surface-electromyography-driven (sEMG) prosthesis. A context-dependent recognition system is proposed in which the same class of sEMG signals may have a different interpretation, depending on the context. This allowed the repertoire of performed movements to be increased. The proposed structure of the context-dependent recognition system includes unambiguously defined decision sequences covering the overall action of the prosthesis, i.e. the so-called boxes. Because the boxes are mutually isolated environments, each box has its own interpretation of the recognition result, as well as a separate local-recognition-task-focused classifier.</p><p><span>Due to the freedom to assign contextual meanings to classes of biosignals, the construction procedure of the classifier can be optimised in terms of the local classification quality in a given box or the classification quality of the entire system. In the paper, two optimisation problems are formulated, differing in the adopted constraints on optimisation variables, with the methods of solving the problems based on an </span>exhaustive search<span> and an evolutionary algorithm, being developed.</span></p><p>Experimental studies were conducted using signals from 1 able-bodied person with simulation of amputation and 10 volunteers with transradial amputations. The study compared the classical recognition system and the context-dependent system for various classifier models. An unusual testing strategy was adopted in the research, taking into account the specificity of the considered recognition task, with two original quality measures resulting from this scheme then being applied. The results obtained confirm the hypothesis that the application of the context-dependent classifier led to an improvement in classification quality.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 161-182"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549143","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}
Heart failure is a chronic and progressive condition characterized by the heart’s inability to pump sufficient blood to meet the body’s metabolic demands. It is a significant public health concern worldwide, associated with high morbidity, mortality, and healthcare costs. For advanced heart failure cases not responding to medical therapy, heart transplantation or mechanical circulatory support with ventricular assist devices (VADs) can be considered. In the specific case of bi-ventricular heart failure a replacement of both ventricles is required. In this context a Total Artificial Heart (TAH) may be proposed as a bridge to transplant solution. Additionally, bi-ventricular assist devices (BiVADs) are available to support both ventricles simultaneously. However Bi-ventricular heart failure management is difficult with poor outcomes. New surgical procedures appear to propose solutions after both ventricle failure. One of these intervention uses two continuous-flow VADs as a total artificial heart after cardiac explantation due to myocardial sarcoma. Unfortunately, this procedure makes patient management very difficult as pulmonary pressures and flow rate are no longer measurable after the surgical procedure. The setting of both pumps is hence a complex task for patient management. This article aims at helping clinicians on patient management undergoing double assistance after cardiac explantation by predicting the different outcomes on the vascular grid for all the possible rotational speed combination using a lumped model. Results provide a range of both pump operating conditions suitable for delivering a physiologically adapted flow to the vascular grid when combined with hypotensive treatments.
心力衰竭是一种慢性进行性疾病,其特点是心脏无法泵出足够的血液来满足人体的代谢需求。它是全球关注的重大公共卫生问题,与高发病率、高死亡率和高医疗成本有关。对于药物治疗无效的晚期心力衰竭病例,可以考虑心脏移植或使用心室辅助装置(VAD)进行机械循环支持。在双心室心力衰竭的特殊情况下,需要替换两个心室。在这种情况下,可以建议使用全人工心脏(TAH)作为移植方案的桥梁。此外,双心室辅助装置(BiVAD)可同时支持两个心室。然而,双心室心力衰竭治疗困难重重,疗效不佳。新的外科手术似乎为双心室衰竭提出了解决方案。其中一种干预方法是在心肌肉瘤导致心脏切除后,使用两个连续流 VAD 作为全人工心脏。遗憾的是,由于手术后无法再测量肺动脉压力和血流速度,因此这种手术给患者管理带来了很大困难。因此,两种泵的设置对于患者管理来说是一项复杂的任务。本文的目的是通过使用集合模型预测所有可能的转速组合在血管网格上产生的不同结果,从而帮助临床医生管理心脏摘除术后接受双重辅助的患者。结果提供了在结合降压治疗时适合向血管网提供生理适应流量的两种泵运行条件的范围。
{"title":"On the prediction of the effect of bi-ventricular assistance after cardiac explantation on the vascular flow physiology: A numerical study","authors":"Louis Marcel , Mathieu Specklin , Smaine Kouidri , Mickael Lescroart , Jean-Louis Hébert","doi":"10.1016/j.bbe.2023.12.005","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.005","url":null,"abstract":"<div><p><span><span>Heart failure is a chronic and progressive condition characterized by the heart’s inability to pump sufficient blood to meet the body’s metabolic demands. It is a significant public health concern worldwide, associated with high morbidity, mortality, and healthcare costs. For advanced heart failure cases not responding to medical therapy, heart transplantation or mechanical circulatory<span> support with ventricular assist devices (VADs) can be considered. In the specific case of bi-ventricular heart failure a replacement of both ventricles is required. In this context a Total Artificial Heart (TAH) may be proposed as a bridge to transplant solution. Additionally, bi-ventricular assist devices (BiVADs) are available to support both ventricles simultaneously. However Bi-ventricular heart failure management is difficult with poor outcomes. New surgical procedures appear to propose solutions after both ventricle failure. One of these intervention uses two continuous-flow VADs as a total artificial heart after cardiac </span></span>explantation due to myocardial sarcoma. Unfortunately, this procedure makes patient management very difficult as pulmonary pressures and flow rate are no longer measurable after the surgical procedure. The setting of both pumps is hence a complex task for patient management. This article aims at helping clinicians on patient management undergoing double assistance after cardiac explantation by predicting the different outcomes on the vascular grid for all the possible </span>rotational speed combination using a lumped model. Results provide a range of both pump operating conditions suitable for delivering a physiologically adapted flow to the vascular grid when combined with hypotensive treatments.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 105-118"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139433595","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 : 2024-01-01DOI: 10.1016/j.bbe.2024.03.002
Dawid Borycki , Egidijus Auksorius , Piotr Węgrzyn , Kamil Liżewski , Sławomir Tomczewski , Ieva Žičkienė , Karolis Adomavičius , Karol Karnowski , Maciej Wojtkowski
Spatiotemporal optical coherence tomography (STOC-T) is the novel modality for high-speed, crosstalk- and aberration-free volumetric imaging of biological tissue in vivo. STOC-T extends the Fourier-Domain holographic Optical Coherence Tomography by the spatial phase modulation that enables the reduction of spatial coherence of the tunable laser. By reducing the spatial coherence of the laser, we suppress coherent noise, and, consequently, improve the imaging depth. Furthermore, we remove geometrical aberrations computationally in postprocessing. We recently demonstrated high-speed, high-resolution STOC-T of human retinal imaging in vivo. Here, we show that the dataset produced by STOC-T can be processed differently to reveal blood flow in the human retina in vivo. To render the blood flow, we first pre-process STOC-T holographic data to access the approximated information about the Doppler-shifted optical field backscattered from the sample. Then, we analyze it using methods from the laser Doppler flowmetry, namely, by analyzing the Doppler broadening caused by moving light scatterers (red blood cells). However, contrary to conventional approaches, we use multiple illumination wavelengths. This enables us to render the structural volumetric and blood flow images from the same dataset concurrently. Our method, denoted as multiwavelength laser Doppler holography (MLDH), links laser Doppler flowmetry with multiwavelength holographic detection to enable noninvasive visualization and possible blood flow quantification at different human retina layers at high speeds and high transverse resolution in vivo.
{"title":"Multiwavelength laser doppler holography (MLDH) in spatiotemporal optical coherence tomography (STOC-T)","authors":"Dawid Borycki , Egidijus Auksorius , Piotr Węgrzyn , Kamil Liżewski , Sławomir Tomczewski , Ieva Žičkienė , Karolis Adomavičius , Karol Karnowski , Maciej Wojtkowski","doi":"10.1016/j.bbe.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.03.002","url":null,"abstract":"<div><p>Spatiotemporal optical coherence tomography (STOC-T) is the novel modality for high-speed, crosstalk- and aberration-free volumetric imaging of biological tissue <em>in vivo</em>. STOC-T extends the Fourier-Domain holographic Optical Coherence Tomography by the spatial phase modulation that enables the reduction of spatial coherence of the tunable laser. By reducing the spatial coherence of the laser, we suppress coherent noise, and, consequently, improve the imaging depth. Furthermore, we remove geometrical aberrations computationally in postprocessing. We recently demonstrated high-speed, high-resolution STOC-T of human retinal imaging <em>in vivo</em>. Here, we show that the dataset produced by STOC-T can be processed differently to reveal blood flow in the human retina <em>in vivo</em>. To render the blood flow, we first pre-process STOC-T holographic data to access the approximated information about the Doppler-shifted optical field backscattered from the sample. Then, we analyze it using methods from the laser Doppler flowmetry, namely, by analyzing the Doppler broadening caused by moving light scatterers (red blood cells). However, contrary to conventional approaches, we use multiple illumination wavelengths. This enables us to render the structural volumetric and blood flow images from the same dataset concurrently. Our method, denoted as multiwavelength laser Doppler holography (MLDH), links laser Doppler flowmetry with multiwavelength holographic detection to enable noninvasive visualization and possible blood flow quantification at different human retina layers at high speeds and high transverse resolution <em>in vivo</em>.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 264-275"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000111/pdfft?md5=fbd875c488dc4651bec90fa168ae6951&pid=1-s2.0-S0208521624000111-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140113616","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}
Quantitative analysis of biomarkers in Optical Coherence Tomography (OCT) images plays an import role in the diagnosis and treatment of retinal diseases. However, biomarker segmentation in retinal OCT images is very hard due to the large variations in size and shape of retinal biomarkers, blurred boundaries, low contrast, and speckle interference. We proposed a novel Multi-scale Local-Global Transformer network (MsLGT-Net) for biomarker segmentation in retinal OCT images. The network combines the proposed Multi-scale Fusion Attention (MFA) module, Local-Global Transformer (LGT) module, and Contrastive Learning Enhancement (CLE) module to tackle the challenges of biomarker segmentation. Specifically, the proposed MFA module aims to enhance the network’s ability to learn multi-scale features of retinal biomarkers by effectively combining the local detail information and contextual semantic information of biomarkers at different scales, and improve the representation ability for different classes of biomarkers. The LGT module is designed to learn local and global information adaptively from multi-scale fused features to address the challenge of small biomarker segmentation. In addition, to distinguish features between different types of retinal biomarkers, we propose the CLE module to enhance the feature representation of different biomarkers. Our proposed method is validated on one public dataset and one local dataset. The experimental results show that the proposed method is more effective than other state-of-the-art methods.
对光学相干断层扫描(OCT)图像中的生物标记进行定量分析,在视网膜疾病的诊断和治疗中发挥着重要作用。然而,由于视网膜生物标记物的大小和形状变化很大、边界模糊、对比度低以及斑点干扰,在视网膜 OCT 图像中进行生物标记物分割非常困难。我们提出了一种用于视网膜 OCT 图像生物标记物分割的新型多尺度局部-全局变换器网络(MsLGT-Net)。该网络结合了所提出的多尺度融合注意(MFA)模块、局部-全局变换器(LGT)模块和对比学习增强(CLE)模块,以应对生物标记物分割的挑战。具体来说,所提出的 MFA 模块旨在通过有效结合不同尺度生物标记的局部细节信息和上下文语义信息,增强网络学习视网膜生物标记多尺度特征的能力,并提高对不同类别生物标记的表征能力。LGT 模块旨在从多尺度融合特征中自适应地学习局部和全局信息,以解决小型生物标记物分割的难题。此外,为了区分不同类型视网膜生物标记物的特征,我们提出了 CLE 模块,以增强不同生物标记物的特征表示能力。我们提出的方法在一个公共数据集和一个本地数据集上进行了验证。实验结果表明,所提出的方法比其他最先进的方法更有效。
{"title":"Multi-scale local-global transformer with contrastive learning for biomarkers segmentation in retinal OCT images","authors":"Xiaoming Liu , Yuanzhe Ding , Ying Zhang , Jinshan Tang","doi":"10.1016/j.bbe.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.02.001","url":null,"abstract":"<div><p>Quantitative analysis of biomarkers in Optical Coherence Tomography (OCT) images plays an import role in the diagnosis and treatment of retinal diseases. However, biomarker segmentation in retinal OCT images is very hard due to the large variations in size and shape of retinal biomarkers, blurred boundaries, low contrast, and speckle interference. We proposed a novel <strong>M</strong>ulti-<strong>s</strong>cale <strong>L</strong>ocal-<strong>G</strong>lobal <strong>T</strong>ransformer network (MsLGT-Net) for biomarker segmentation in retinal OCT images. The network combines the proposed <strong>M</strong>ulti-scale <strong>F</strong>usion <strong>A</strong>ttention (MFA) module, <strong>L</strong>ocal-<strong>G</strong>lobal <strong>T</strong>ransformer (LGT) module, and <strong>C</strong>ontrastive <strong>L</strong>earning <strong>E</strong>nhancement (CLE) module to tackle the challenges of biomarker segmentation. Specifically, the proposed MFA module aims to enhance the network’s ability to learn multi-scale features of retinal biomarkers by effectively combining the local detail information and contextual semantic information of biomarkers at different scales, and improve the representation ability for different classes of biomarkers. The LGT module is designed to learn local and global information adaptively from multi-scale fused features to address the challenge of small biomarker segmentation. In addition, to distinguish features between different types of retinal biomarkers, we propose the CLE module to enhance the feature representation of different biomarkers. Our proposed method is validated on one public dataset and one local dataset. The experimental results show that the proposed method is more effective than other state-of-the-art methods.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 231-246"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139743583","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 : 2024-01-01DOI: 10.1016/j.bbe.2023.12.006
Aleksandra Kuls-Oszmaniec , Michał Kacprzak , Magdalena Morawiec , Piotr Sawosz , Urszula Fiszer , Marta Leńska-Mieciek
Stroke is a leading cause of disability and death worldwide, with acute ischemic stroke (AIS) accounting for the majority of cases. Early and accurate diagnosis of AIS is crucial for improving patient outcomes. Non-invasive monitoring techniques, such as time domain near-infrared spectroscopy (tdNIRS), have shown potential for real-time monitoring of AIS patients at the bedside. However, there is a need for further research to evaluate the effectiveness of tdNIRS in the acute phase of stroke. In this study, we present the results of a case report using tdNIRS to monitor AIS patients without any additional stimulation. The tdNIRS technique allows for non-invasively assessing cerebral oxygenation in absolute units, enabling accurate measurement of changes in oxygenated and deoxygenated hemoglobin concentrations in the brain. Our aim was to determine the feasibility of tdNIRS in monitoring AIS patients.
{"title":"Time-resolved near-infrared spectroscopy in monitoring acute ischemic stroke patients – Case study","authors":"Aleksandra Kuls-Oszmaniec , Michał Kacprzak , Magdalena Morawiec , Piotr Sawosz , Urszula Fiszer , Marta Leńska-Mieciek","doi":"10.1016/j.bbe.2023.12.006","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.006","url":null,"abstract":"<div><p>Stroke is a leading cause of disability and death worldwide, with acute ischemic stroke<span> (AIS) accounting for the majority of cases. Early and accurate diagnosis of AIS is crucial for improving patient outcomes. Non-invasive monitoring techniques, such as time domain near-infrared spectroscopy (tdNIRS), have shown potential for real-time monitoring of AIS patients at the bedside. However, there is a need for further research to evaluate the effectiveness of tdNIRS in the acute phase of stroke. In this study, we present the results of a case report using tdNIRS to monitor AIS patients without any additional stimulation. The tdNIRS technique allows for non-invasively assessing cerebral oxygenation in absolute units, enabling accurate measurement of changes in oxygenated and deoxygenated hemoglobin concentrations in the brain. Our aim was to determine the feasibility of tdNIRS in monitoring AIS patients.</span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 149-160"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139503513","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 : 2024-01-01DOI: 10.1016/j.bbe.2024.01.005
Chongguang Wang , Kerrie Evans , Dean Hartley , Scott Morrison , Martin Veidt , Gui Wang
Plantar pressure distribution offers insights into foot function, gait mechanics, and foot-related issues. This systematic review presents an analysis of the use of artificial neural network techniques in the context of plantar pressure analysis. 60 studies were included in the review. Sample size, pathology, pressure sensor number, data collection device, utilization of other sensor devices, ground-truth methods, pre-processing dataset, neural network type, and evaluation metrics were evaluated. Utilization of customized wearable footwear devices for the acquisition of data was common amongst both healthy participants and patients. Inertial measurement units emerged as an effective compensatory measure to address the limitations associated with the distribution of plantar pressure. Ground truth methods predominantly relied on the usage of both annotations and reference devices. Multilayer perceptron, convolutional neural networks, and recurrent neural networks were identified as the most frequently employed artificial neural network algorithms across the reviewed studies. Finally, the evaluation of performance largely drew upon statistical descriptions and other machine learning methods. This review provides a comprehensive understanding of the use of artificial neural network techniques in plantar pressure analysis, highlighting opportunities for future research.
{"title":"A systematic review of artificial neural network techniques for analysis of foot plantar pressure","authors":"Chongguang Wang , Kerrie Evans , Dean Hartley , Scott Morrison , Martin Veidt , Gui Wang","doi":"10.1016/j.bbe.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.005","url":null,"abstract":"<div><p>Plantar pressure distribution offers insights into foot function, gait mechanics, and foot-related issues. This systematic review presents an analysis of the use of artificial neural network techniques in the context of plantar pressure analysis. 60 studies were included in the review. Sample size, pathology, pressure sensor number, data collection device, utilization of other sensor devices, ground-truth methods, pre-processing dataset, neural network type, and evaluation metrics were evaluated. Utilization of customized wearable footwear devices for the acquisition of data was common amongst both healthy participants and patients. Inertial measurement units emerged as an effective compensatory measure to address the limitations associated with the distribution of plantar pressure. Ground truth methods predominantly relied on the usage of both annotations and reference devices. Multilayer perceptron, convolutional neural networks, and recurrent neural networks were identified as the most frequently employed artificial neural network algorithms across the reviewed studies. Finally, the evaluation of performance largely drew upon statistical descriptions and other machine learning methods. This review provides a comprehensive understanding of the use of artificial neural network techniques in plantar pressure analysis, highlighting opportunities for future research.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 197-208"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000056/pdfft?md5=9b7f95a54d7af9620bb2a34de80b906f&pid=1-s2.0-S0208521624000056-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139674820","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 : 2024-01-01DOI: 10.1016/j.bbe.2024.01.004
Emanuela Formaggio , Lucio Pastena , Massimo Melucci , Lucio Ricciardi , Silvia Francesca Storti
In this study, we investigated the effects of oxygen toxicity on brain activity and functional connectivity (FC) in divers using a closed-circuit oxygen breathing apparatus. We acquired and analyzed electroencephalographic (EEG) signals from a group of normal professional divers (PD) and a group that developed oxygen intolerance, i.e., oxygen-intolerant professional divers (OPD), to evaluate the potential risk of a dive and understand the physiological mechanisms involved. The results highlighted a significant difference in the baseline levels of rhythm between PD and OPD, with PD exhibiting a lower level to counteract the effects of increased inhalation, while OPD showed a higher level that resulted in a pathological state. Connectivity analysis revealed a strong correlation between cognitive and motor regions, and high levels of synchronization at rest in OPDs. Our findings suggest that a pathological condition may underlie the higher levels observed in these individuals when facing the stress of high inhalation. These findings support the hypothesis that oxygen modulates brain networks, and have important implications for understanding the neural mechanisms involved in oxygen toxicity. The study also provides a unique opportunity to investigate the impact of neurophysiological activity in simulated critical scenarios, and opens up new perspectives in the screening and monitoring of divers.
{"title":"Disruptions in brain functional connectivity: The hidden risk for oxygen-intolerant professional divers in simulated deep water","authors":"Emanuela Formaggio , Lucio Pastena , Massimo Melucci , Lucio Ricciardi , Silvia Francesca Storti","doi":"10.1016/j.bbe.2024.01.004","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.01.004","url":null,"abstract":"<div><p>In this study, we investigated the effects of oxygen toxicity on brain activity and functional connectivity (FC) in divers using a closed-circuit oxygen breathing apparatus. We acquired and analyzed electroencephalographic (EEG) signals from a group of normal professional divers (PD) and a group that developed oxygen intolerance, i.e., oxygen-intolerant professional divers (OPD), to evaluate the potential risk of a dive and understand the physiological mechanisms involved. The results highlighted a significant difference in the baseline levels of <span><math><mi>α</mi></math></span> rhythm between PD and OPD, with PD exhibiting a lower level to counteract the effects of increased <span><math><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> inhalation, while OPD showed a higher level that resulted in a pathological state. Connectivity analysis revealed a strong correlation between cognitive and motor regions, and high levels of <span><math><mi>α</mi></math></span> synchronization at rest in OPDs. Our findings suggest that a pathological condition may underlie the higher <span><math><mi>α</mi></math></span> levels observed in these individuals when facing the stress of high <span><math><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> inhalation. These findings support the hypothesis that oxygen modulates brain networks, and have important implications for understanding the neural mechanisms involved in oxygen toxicity. The study also provides a unique opportunity to investigate the impact of neurophysiological activity in simulated critical scenarios, and opens up new perspectives in the screening and monitoring of divers.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 209-217"},"PeriodicalIF":6.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000044/pdfft?md5=5edb8ca083818f99257fa9753df94806&pid=1-s2.0-S0208521624000044-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139709621","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 : 2023-12-27DOI: 10.1016/j.bbe.2023.12.001
Arpita Panigrahi , Hemant Sharma , Atin Mukherjee
Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial RGB videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the principal component analysis to derive a pulsating component representing the blood volumetric changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios.
{"title":"Video-based HR measurement using adaptive facial regions with multiple color spaces","authors":"Arpita Panigrahi , Hemant Sharma , Atin Mukherjee","doi":"10.1016/j.bbe.2023.12.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.001","url":null,"abstract":"<div><p><span><span><span>Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial </span>RGB<span> videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the </span></span>principal component analysis to derive a pulsating component representing the blood </span>volumetric<span> changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios.</span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 68-82"},"PeriodicalIF":6.4,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050378","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 : 2023-12-26DOI: 10.1016/j.bbe.2023.12.004
Janusz Krzymien , Piotr Ladyzynski
Insulin resistance (IR) is a multifactorial metabolic disorder associated with the development of cardiometabolic syndrome, cardiovascular diseases and obesity. Factors such as inflammation, hyperinsulinemia, hyperglucagonemia, mitochondrial dysfunction, glucotoxicity and lipotoxicity contribute to the development of IR. Despite being extensively studied for over 60 years, assessing the incidence of IR, developing effective prevention strategies, and implementing appropriate therapeutic approaches remain challenging. This review explores the multifaceted nature of IR, including its association with various conditions such as obesity, primary hypertension, dyslipidemia, obstructive sleep apnea, Alzheimer's disease, non-alcoholic fatty liver disease, polycystic ovary syndrome, chronic kidney disease and cancer. Additionally, we discuss the complexity of diagnosing and quantifying IR, emphasizing the lack of absolute, common criteria for classification. We delve into the use of mathematical models in clinical and epidemiological studies, focusing on the choice between insulin, triglycerides, or waist-to-hip ratio as IR determinants. Furthermore, we highlight the importance of reliable input data and caution in interpreting results when utilizing mathematical models for IR assessment. This narrative review aims to provide insights into the challenges and considerations involved in conducting IR diagnostics, with implications for clinical practice, epidemiological research, and future advancements in this field.
胰岛素抵抗(IR)是一种多因素代谢紊乱,与心血管代谢综合征、心血管疾病和肥胖症的发生有关。炎症、高胰岛素血症、高胰高血糖素血症、线粒体功能障碍、葡萄糖毒性和脂肪毒性等因素都会导致胰岛素抵抗的发生。尽管 60 多年来对 IR 进行了广泛的研究,但评估 IR 的发病率、制定有效的预防策略和实施适当的治疗方法仍具有挑战性。本综述探讨了 IR 的多面性,包括它与肥胖、原发性高血压、血脂异常、阻塞性睡眠呼吸暂停、阿尔茨海默病、非酒精性脂肪肝、多囊卵巢综合征、慢性肾病和癌症等各种疾病的关联。此外,我们还讨论了诊断和量化 IR 的复杂性,强调缺乏绝对、通用的分类标准。我们深入探讨了数学模型在临床和流行病学研究中的应用,重点关注胰岛素、甘油三酯或腰臀比作为 IR 决定因素的选择。此外,我们还强调了可靠输入数据的重要性,以及在利用数学模型进行 IR 评估时谨慎解释结果的重要性。这篇叙述性综述旨在深入探讨进行红外诊断所面临的挑战和需要考虑的因素,并对临床实践、流行病学研究和该领域的未来发展产生影响。
{"title":"Insulin resistance: Risk factors, diagnostic approaches and mathematical models for clinical practice, epidemiological studies, and beyond","authors":"Janusz Krzymien , Piotr Ladyzynski","doi":"10.1016/j.bbe.2023.12.004","DOIUrl":"https://doi.org/10.1016/j.bbe.2023.12.004","url":null,"abstract":"<div><p><span>Insulin resistance (IR) is a multifactorial metabolic disorder associated with the development of cardiometabolic syndrome, cardiovascular diseases and obesity. Factors such as inflammation, </span>hyperinsulinemia<span>, hyperglucagonemia, mitochondrial dysfunction, glucotoxicity and lipotoxicity<span><span> contribute to the development of IR. Despite being extensively studied for over 60 years, assessing the incidence of IR, developing effective prevention strategies, and implementing appropriate therapeutic approaches remain challenging. This review explores the multifaceted nature of IR, including its association with various conditions such as obesity, primary hypertension, dyslipidemia, obstructive sleep apnea, </span>Alzheimer's disease<span>, non-alcoholic fatty liver disease, polycystic ovary syndrome, chronic kidney disease and cancer. Additionally, we discuss the complexity of diagnosing and quantifying IR, emphasizing the lack of absolute, common criteria for classification. We delve into the use of mathematical models<span> in clinical and epidemiological studies<span>, focusing on the choice between insulin, triglycerides, or waist-to-hip ratio as IR determinants. Furthermore, we highlight the importance of reliable input data and caution in interpreting results when utilizing mathematical models for IR assessment. This narrative review aims to provide insights into the challenges and considerations involved in conducting IR diagnostics, with implications for clinical practice, epidemiological research, and future advancements in this field.</span></span></span></span></span></p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 1","pages":"Pages 55-67"},"PeriodicalIF":6.4,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050400","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}