Pub Date : 2023-11-13DOI: 10.1109/RBME.2023.3332164
Bin He
2023 has been a year of growth and transformation for IEEE Reviews in Biomedical Engineering (RBME). Thanks to our authors, reviewers, and editorial board members, RBME received strong metrics on Impact Factor and CiteScore reaching 17.6 and 27.8 respectively, which places RBME in the top 3 according to the Impact Factor, and the top 4 according to the CiteScore in all Biomedical Engineering Journals/Publications. We have also observed substantially increasing submissions in the past year. To better serve our authors, we have implemented a screening process to quickly communicate the outcome of assessment, and allow the authors to submit manuscripts which do not fit the scope or have a low chance of passing through the highly selective review process, to find a more suitable journal in a timely manner.
{"title":"Editorial: On the Writing of a Scientific Review Article","authors":"Bin He","doi":"10.1109/RBME.2023.3332164","DOIUrl":"10.1109/RBME.2023.3332164","url":null,"abstract":"2023 has been a year of growth and transformation for IEEE Reviews in Biomedical Engineering (RBME). Thanks to our authors, reviewers, and editorial board members, RBME received strong metrics on Impact Factor and CiteScore reaching 17.6 and 27.8 respectively, which places RBME in the top 3 according to the Impact Factor, and the top 4 according to the CiteScore in all Biomedical Engineering Journals/Publications. We have also observed substantially increasing submissions in the past year. To better serve our authors, we have implemented a screening process to quickly communicate the outcome of assessment, and allow the authors to submit manuscripts which do not fit the scope or have a low chance of passing through the highly selective review process, to find a more suitable journal in a timely manner.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"3-3"},"PeriodicalIF":17.6,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10315188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92156913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-09DOI: 10.1109/RBME.2023.3331297
Peter G. Jacobs;Pau Herrero;Andrea Facchinetti;Josep Vehi;Boris Kovatchev;Marc D. Breton;Ali Cinar;Konstantina S. Nikita;Francis J. Doyle;Jorge Bondia;Tadej Battelino;Jessica R. Castle;Konstantia Zarkogianni;Rahul Narayan;Clara Mosquera-Lopez
Objective: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. Methods: Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. Significance: These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.
{"title":"Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities","authors":"Peter G. Jacobs;Pau Herrero;Andrea Facchinetti;Josep Vehi;Boris Kovatchev;Marc D. Breton;Ali Cinar;Konstantina S. Nikita;Francis J. Doyle;Jorge Bondia;Tadej Battelino;Jessica R. Castle;Konstantia Zarkogianni;Rahul Narayan;Clara Mosquera-Lopez","doi":"10.1109/RBME.2023.3331297","DOIUrl":"10.1109/RBME.2023.3331297","url":null,"abstract":"<italic>Objective:</i>\u0000 Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. \u0000<italic>Methods:</i>\u0000 Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. \u0000<italic>Significance:</i>\u0000 These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"19-41"},"PeriodicalIF":17.6,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10313965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72015670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/RBME.2023.3324264
Li Tong;Wenqi Shi;Monica Isgut;Yishan Zhong;Peter Lais;Logan Gloster;Jimin Sun;Aniketh Swain;Felipe Giuste;May D. Wang
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
{"title":"Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence","authors":"Li Tong;Wenqi Shi;Monica Isgut;Yishan Zhong;Peter Lais;Logan Gloster;Jimin Sun;Aniketh Swain;Felipe Giuste;May D. Wang","doi":"10.1109/RBME.2023.3324264","DOIUrl":"10.1109/RBME.2023.3324264","url":null,"abstract":"With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"80-97"},"PeriodicalIF":17.6,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10283869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41215373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.1109/RBME.2023.3309328
F. Barberi;E. Anselmino;A. Mazzoni;M. Goldfarb;S. Micera
The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users’ needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.
{"title":"Toward the Development of User-Centered Neurointegrated Lower Limb Prostheses","authors":"F. Barberi;E. Anselmino;A. Mazzoni;M. Goldfarb;S. Micera","doi":"10.1109/RBME.2023.3309328","DOIUrl":"10.1109/RBME.2023.3309328","url":null,"abstract":"The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users’ needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"212-228"},"PeriodicalIF":17.6,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10232905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10483877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-21DOI: 10.1109/RBME.2023.3297604
Hongming Xu;Qi Xu;Fengyu Cong;Jeonghyun Kang;Chu Han;Zaiyi Liu;Anant Madabhushi;Cheng Lu
Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This article presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.
{"title":"Vision Transformers for Computational Histopathology","authors":"Hongming Xu;Qi Xu;Fengyu Cong;Jeonghyun Kang;Chu Han;Zaiyi Liu;Anant Madabhushi;Cheng Lu","doi":"10.1109/RBME.2023.3297604","DOIUrl":"10.1109/RBME.2023.3297604","url":null,"abstract":"Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This article presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"63-79"},"PeriodicalIF":17.6,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10227147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-20DOI: 10.1109/RBME.2023.3296938
Xiao Gu;Fani Deligianni;Jinpei Han;Xiangyu Liu;Wei Chen;Guang-Zhong Yang;Benny Lo
The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely scarcity , quality , and heterogeneity , hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this article, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.
{"title":"Beyond Supervised Learning for Pervasive Healthcare","authors":"Xiao Gu;Fani Deligianni;Jinpei Han;Xiangyu Liu;Wei Chen;Guang-Zhong Yang;Benny Lo","doi":"10.1109/RBME.2023.3296938","DOIUrl":"10.1109/RBME.2023.3296938","url":null,"abstract":"The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely \u0000<italic>scarcity</i>\u0000, \u0000<italic>quality</i>\u0000, and \u0000<italic>heterogeneity</i>\u0000, hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this article, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"42-62"},"PeriodicalIF":17.6,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9962013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.1109/RBME.2023.3283149
Fotios S. Konstantakopoulos;Eleni I. Georga;Dimitrios I. Fotiadis
The daily healthy diet and balanced intake of essential nutrients play an important role in modern lifestyle. The estimation of a meal's nutrient content is an integral component of significant diseases, such as diabetes, obesity and cardiovascular disease. Lately, there has been an increasing interest towards the development and utilization of smartphone applications with the aim of promoting healthy behaviours. The semi – automatic or automatic, precise and in real-time estimation of the nutrients of daily consumed meals is approached in relevant literature as a computer vision problem using food images which are taken via a user's smartphone. Herein, we present the state-of-the-art on automatic food recognition and food volume estimation methods starting from their basis, i.e., the food image databases. First, by methodically organizing the extracted information from the reviewed studies, this review study enables the comprehensive fair assessment of the methods and techniques applied for segmenting food images, classifying their food content and computing the food volume, associating their results with the characteristics of the used datasets. Second, by unbiasedly reporting the strengths and limitations of these methods and proposing pragmatic solutions to the latter, this review can inspire future directions in the field of dietary assessment systems.
{"title":"A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems","authors":"Fotios S. Konstantakopoulos;Eleni I. Georga;Dimitrios I. Fotiadis","doi":"10.1109/RBME.2023.3283149","DOIUrl":"10.1109/RBME.2023.3283149","url":null,"abstract":"The daily healthy diet and balanced intake of essential nutrients play an important role in modern lifestyle. The estimation of a meal's nutrient content is an integral component of significant diseases, such as diabetes, obesity and cardiovascular disease. Lately, there has been an increasing interest towards the development and utilization of smartphone applications with the aim of promoting healthy behaviours. The semi – automatic or automatic, precise and in real-time estimation of the nutrients of daily consumed meals is approached in relevant literature as a computer vision problem using food images which are taken via a user's smartphone. Herein, we present the state-of-the-art on automatic food recognition and food volume estimation methods starting from their basis, i.e., the food image databases. First, by methodically organizing the extracted information from the reviewed studies, this review study enables the comprehensive fair assessment of the methods and techniques applied for segmenting food images, classifying their food content and computing the food volume, associating their results with the characteristics of the used datasets. Second, by unbiasedly reporting the strengths and limitations of these methods and proposing pragmatic solutions to the latter, this review can inspire future directions in the field of dietary assessment systems.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"136-152"},"PeriodicalIF":17.6,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10144465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9934987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.
{"title":"Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review","authors":"Zipei Wang;Mengjie Fang;Jie Zhang;Linquan Tang;Lianzhen Zhong;Hailin Li;Runnan Cao;Xun Zhao;Shengyuan Liu;Ruofan Zhang;Xuebin Xie;Haiqiang Mai;Sufang Qiu;Jie Tian;Di Dong","doi":"10.1109/RBME.2023.3269776","DOIUrl":"10.1109/RBME.2023.3269776","url":null,"abstract":"Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"118-135"},"PeriodicalIF":17.6,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9337472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-24DOI: 10.1109/RBME.2023.3279655
Abu Ilius Faisal;Tapas Mondal;M. Jamal Deen
Human gait analysis aims to assess gait mechanics and to identify the deviations from “normal” gait patterns by using meaningful parameters extracted from gait data. As each parameter indicates different gait characteristics, a proper combination of key parameters is required to perform an overall gait assessment. Therefore, in this study, we introduced a simple gait index derived from the most important gait parameters (walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio) to quantify overall gait quality. We performed a systematic review to select the parameters and analyzed a gait dataset (120 healthy subjects) to develop the index and to determine the healthy range (0.50 – 0.67). To validate the parameter selection and to justify the defined index range, we applied a support vector machine algorithm to classify the dataset based on the selected parameters and achieved a high classification accuracy (∼95%). Also, we explored other published datasets that are in good agreement with the proposed index prediction, reinforcing the reliability and effectiveness of the developed gait index. The gait index can be used as a reference for preliminary assessment of human gait conditions and to quickly identify abnormal gait patterns and possible relation to health issues.
{"title":"Systematic Development of a Simple Human Gait Index","authors":"Abu Ilius Faisal;Tapas Mondal;M. Jamal Deen","doi":"10.1109/RBME.2023.3279655","DOIUrl":"10.1109/RBME.2023.3279655","url":null,"abstract":"Human gait analysis aims to assess gait mechanics and to identify the deviations from “normal” gait patterns by using meaningful parameters extracted from gait data. As each parameter indicates different gait characteristics, a proper combination of key parameters is required to perform an overall gait assessment. Therefore, in this study, we introduced a simple gait index derived from the most important gait parameters (walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio) to quantify overall gait quality. We performed a systematic review to select the parameters and analyzed a gait dataset (120 healthy subjects) to develop the index and to determine the healthy range (0.50 – 0.67). To validate the parameter selection and to justify the defined index range, we applied a support vector machine algorithm to classify the dataset based on the selected parameters and achieved a high classification accuracy (∼95%). Also, we explored other published datasets that are in good agreement with the proposed index prediction, reinforcing the reliability and effectiveness of the developed gait index. The gait index can be used as a reference for preliminary assessment of human gait conditions and to quickly identify abnormal gait patterns and possible relation to health issues.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"229-242"},"PeriodicalIF":17.6,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9517689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-15DOI: 10.1109/RBME.2023.3271595
Lei Lu;Tingting Zhu;Davide Morelli;Andrew Creagh;Zhangdaihong Liu;Jenny Yang;Fenglin Liu;Yuan-Ting Zhang;David A. Clifton
Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.
{"title":"Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review","authors":"Lei Lu;Tingting Zhu;Davide Morelli;Andrew Creagh;Zhangdaihong Liu;Jenny Yang;Fenglin Liu;Yuan-Ting Zhang;David A. Clifton","doi":"10.1109/RBME.2023.3271595","DOIUrl":"10.1109/RBME.2023.3271595","url":null,"abstract":"Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"180-196"},"PeriodicalIF":17.6,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10124275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9471426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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