Pub Date : 2022-09-27DOI: 10.1109/RBME.2022.3210015
Yang Yang;Zhiguo He;Pengcheng Jiao;Hongliang Ren
Soft robotics has opened a unique path to flexibility and environmental adaptability, learning from nature and reproducing biological behaviors. Nature implies answers for how to apply robots to real life. To find out how we learn from creatures to design and apply soft robots, in this Review, we propose a classification method to summarize soft robots based on different functions of biological systems: self-growing, self-healing, self-responsive, and self-circulatory. The bio-function based classification logic is presented to explain why we learn from creatures. State-of-art technologies, characteristics, pros, cons, challenges, and potential applications of these categories are analyzed to illustrate what we learned from creatures. By intersecting these categories, the existing and potential bio-inspired applications are overviewed and outlooked to finally find the answer, that is, how we learn from creatures.
{"title":"Bioinspired Soft Robotics: How Do We Learn From Creatures?","authors":"Yang Yang;Zhiguo He;Pengcheng Jiao;Hongliang Ren","doi":"10.1109/RBME.2022.3210015","DOIUrl":"10.1109/RBME.2022.3210015","url":null,"abstract":"Soft robotics has opened a unique path to flexibility and environmental adaptability, learning from nature and reproducing biological behaviors. Nature implies answers for how to apply robots to real life. To find out how we learn from creatures to design and apply soft robots, in this Review, we propose a classification method to summarize soft robots based on different functions of biological systems: self-growing, self-healing, self-responsive, and self-circulatory. The bio-function based classification logic is presented to explain \u0000<italic>why</i>\u0000 we learn from creatures. State-of-art technologies, characteristics, pros, cons, challenges, and potential applications of these categories are analyzed to illustrate \u0000<italic>what</i>\u0000 we learned from creatures. By intersecting these categories, the existing and potential bio-inspired applications are overviewed and outlooked to finally find the answer, that is, \u0000<italic>how</i>\u0000 we learn from creatures.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"153-165"},"PeriodicalIF":17.6,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40378236","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 : 2022-07-01DOI: 10.1109/RBME.2022.3187840
Thomas H. Nown;Priti Upadhyay;Andrew Kerr;Ivan Andonovic;Christos Tachtatzis;Madeleine A. Grealy
Movement sonification is emerging as a useful tool for rehabilitation, with increasing evidence in support of its use. To create such a system requires component considerations outside of typical sonification design choices, such as the dimension of movement to sonify, section of anatomy to track, and methodology of motion capture. This review takes this emerging and highly diverse area of literature and keyword-code existing real-time movement sonification systems, to analyze and highlight current trends in these design choices, as such providing an overview of existing systems. A combination of snowballing through relevant existing reviews and a systematic search of multiple databases were utilized to obtain a list of projects for data extraction. The review categorizes systems into three sections: identifying the link between physical dimension to auditory dimension used in sonification, identifying the target anatomy tracked, identifying the movement tracking system used to monitor the target anatomy. The review proceeds to analyze the systematic mapping of the literature and provide results of the data analysis highlighting common and innovative design choices used, irrespective of application, before discussing the findings in the context of movement rehabilitation. A database containing the mapped keywords assigned to each project are submitted with this review.
{"title":"A Mapping Review of Real-Time Movement Sonification Systems for Movement Rehabilitation","authors":"Thomas H. Nown;Priti Upadhyay;Andrew Kerr;Ivan Andonovic;Christos Tachtatzis;Madeleine A. Grealy","doi":"10.1109/RBME.2022.3187840","DOIUrl":"10.1109/RBME.2022.3187840","url":null,"abstract":"Movement sonification is emerging as a useful tool for rehabilitation, with increasing evidence in support of its use. To create such a system requires component considerations outside of typical sonification design choices, such as the dimension of movement to sonify, section of anatomy to track, and methodology of motion capture. This review takes this emerging and highly diverse area of literature and keyword-code existing real-time movement sonification systems, to analyze and highlight current trends in these design choices, as such providing an overview of existing systems. A combination of snowballing through relevant existing reviews and a systematic search of multiple databases were utilized to obtain a list of projects for data extraction. The review categorizes systems into three sections: identifying the link between physical dimension to auditory dimension used in sonification, identifying the target anatomy tracked, identifying the movement tracking system used to monitor the target anatomy. The review proceeds to analyze the systematic mapping of the literature and provide results of the data analysis highlighting common and innovative design choices used, irrespective of application, before discussing the findings in the context of movement rehabilitation. A database containing the mapped keywords assigned to each project are submitted with this review.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"672-686"},"PeriodicalIF":17.6,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9365636","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 : 2022-06-28DOI: 10.1109/RBME.2022.3186828
Iyabosola B. Oronti;Ernesto Iadanza;Leandro Pecchia
Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear.
{"title":"Hypertension Diagnosis and Management in Africa Using Mobile Phones: A Scoping Review","authors":"Iyabosola B. Oronti;Ernesto Iadanza;Leandro Pecchia","doi":"10.1109/RBME.2022.3186828","DOIUrl":"10.1109/RBME.2022.3186828","url":null,"abstract":"Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). The majority of investigations on NCDs agree that the leading risk factor for mortality worldwide is hypertension. Over 75% of the world's mobile phone subscriptions reside in LMICs, hence making the mobile phone particularly relevant to mHealth deployment in Africa. This study is aimed at determining the scope of the literature available on hypertension diagnosis and management in Africa, with particular emphasis on determining the feasibility, acceptability and effectiveness of interventions based on the use of mobile phones. The bulk of the evidence considered overwhelmingly shows that SMS technology is yet the most used medium for executing interventions in Africa. Consequently, the need to define novel and superior ways of providing effective and low-cost monitoring, diagnosis, and management of hypertension-related NCDs delivered through artificial intelligence and machine learning techniques is clear.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"197-211"},"PeriodicalIF":17.6,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9809807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40407703","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 : 2022-06-23DOI: 10.1109/RBME.2022.3185292
Toufique A. Soomro;Lihong Zheng;Ahmed J. Afifi;Ahmed Ali;Shafiullah Soomro;Ming Yin;Junbin Gao
Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.
{"title":"Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review","authors":"Toufique A. Soomro;Lihong Zheng;Ahmed J. Afifi;Ahmed Ali;Shafiullah Soomro;Ming Yin;Junbin Gao","doi":"10.1109/RBME.2022.3185292","DOIUrl":"10.1109/RBME.2022.3185292","url":null,"abstract":"Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"70-90"},"PeriodicalIF":17.6,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9364548","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 : 2022-06-23DOI: 10.1109/RBME.2022.3185953
Felipe Giuste;Wenqi Shi;Yuanda Zhu;Tarun Naren;Monica Isgut;Ying Sha;Li Tong;Mitali Gupte;May D. Wang
Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decision-making. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.
{"title":"Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review","authors":"Felipe Giuste;Wenqi Shi;Yuanda Zhu;Tarun Naren;Monica Isgut;Ying Sha;Li Tong;Mitali Gupte;May D. Wang","doi":"10.1109/RBME.2022.3185953","DOIUrl":"10.1109/RBME.2022.3185953","url":null,"abstract":"Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decision-making. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"5-21"},"PeriodicalIF":17.6,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09804787.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9364551","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 : 2022-06-16DOI: 10.1109/RBME.2022.3183852
Ephraim Nwoye;Wai Lok Woo;Bin Gao;Tobenna Anyanwu
Surgery is a high-risk procedure of therapy and is associated to post trauma complications of longer hospital stay, estimated blood loss and long duration of surgeries. Reports have suggested that over 2.5% patients die during and post operation. This paper is aimed at systematic review of previous research on artificial intelligence (AI) in surgery, analyzing their results with suitable software to validate their research by obtaining same or contrary results. Six published research articles have been reviewed across three continents. These articles have been re-validated using software including SPSS and MedCalc to obtain the statistical features such as the mean, standard deviation, significant level, and standard error. From the significant values, the experiments are then classified according to the null (p < 0.05) or alternative (p>0.05) hypotheses. The results obtained from the analysis have suggested significant difference in operating time, docking time, staging time, and estimated blood loss but show no significant difference in length of hospital stay, recovery time and lymph nodes harvested between robotic assisted surgery using AI and normal conventional surgery. From the evaluations, this research suggests that AI-assisted surgery improves over the conventional surgery as safer and more efficient system of surgery with minimal or no complications.
{"title":"Artificial Intelligence for Emerging Technology in Surgery: Systematic Review and Validation","authors":"Ephraim Nwoye;Wai Lok Woo;Bin Gao;Tobenna Anyanwu","doi":"10.1109/RBME.2022.3183852","DOIUrl":"10.1109/RBME.2022.3183852","url":null,"abstract":"Surgery is a high-risk procedure of therapy and is associated to post trauma complications of longer hospital stay, estimated blood loss and long duration of surgeries. Reports have suggested that over 2.5% patients die during and post operation. This paper is aimed at systematic review of previous research on artificial intelligence (AI) in surgery, analyzing their results with suitable software to validate their research by obtaining same or contrary results. Six published research articles have been reviewed across three continents. These articles have been re-validated using software including SPSS and MedCalc to obtain the statistical features such as the mean, standard deviation, significant level, and standard error. From the significant values, the experiments are then classified according to the null (p < 0.05) or alternative (p>0.05) hypotheses. The results obtained from the analysis have suggested significant difference in operating time, docking time, staging time, and estimated blood loss but show no significant difference in length of hospital stay, recovery time and lymph nodes harvested between robotic assisted surgery using AI and normal conventional surgery. From the evaluations, this research suggests that AI-assisted surgery improves over the conventional surgery as safer and more efficient system of surgery with minimal or no complications.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"241-259"},"PeriodicalIF":17.6,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9359911","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 : 2022-06-10DOI: 10.1109/RBME.2022.3181700
Arif Mohd. Kamal;Tushar Sakorikar;Uttam M. Pal;Hardik J. Pandya
Breast cancer is a leading cause of mortality among women. The patient's survival rate is uncertain due to the limitations in the accuracy of diagnosis and effective monitoring during cancer treatment. The key to efficaciously controlling cancer on a larger scale is effective diagnosis at an early stage of cancer by distinguishing the vital signatures of the diseased from the normal breast tissue. The breast tissue is a heterogeneous turbid media that exhibits multifaceted bulk tissue properties. Various sensing modalities can yield distinct tissue behavior for cancer and adjacent normal tissues, serving as a basis for cancer diagnosis. A novel multimodal diagnostic tool that can concurrently assess the optical, electrical, and mechanical bulk tissue properties can substantially augment the clinical findings such as histopathology, potentially aiding the clinician to establish an accurate and rapid diagnosis of cancer. This review aims to discuss the clinical and engineering aspects along with the unmet challenges of these physical sensing modalities, primarily in the field of optical, electrical, and mechanical. The challenges of combining two or more of these sensing modalities that can significantly enhance the effectiveness of the clinical diagnostic tools are further investigated.
{"title":"Engineering Approaches for Breast Cancer Diagnosis: A Review","authors":"Arif Mohd. Kamal;Tushar Sakorikar;Uttam M. Pal;Hardik J. Pandya","doi":"10.1109/RBME.2022.3181700","DOIUrl":"10.1109/RBME.2022.3181700","url":null,"abstract":"Breast cancer is a leading cause of mortality among women. The patient's survival rate is uncertain due to the limitations in the accuracy of diagnosis and effective monitoring during cancer treatment. The key to efficaciously controlling cancer on a larger scale is effective diagnosis at an early stage of cancer by distinguishing the vital signatures of the diseased from the normal breast tissue. The breast tissue is a heterogeneous turbid media that exhibits multifaceted bulk tissue properties. Various sensing modalities can yield distinct tissue behavior for cancer and adjacent normal tissues, serving as a basis for cancer diagnosis. A novel multimodal diagnostic tool that can concurrently assess the optical, electrical, and mechanical bulk tissue properties can substantially augment the clinical findings such as histopathology, potentially aiding the clinician to establish an accurate and rapid diagnosis of cancer. This review aims to discuss the clinical and engineering aspects along with the unmet challenges of these physical sensing modalities, primarily in the field of optical, electrical, and mechanical. The challenges of combining two or more of these sensing modalities that can significantly enhance the effectiveness of the clinical diagnostic tools are further investigated.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"687-705"},"PeriodicalIF":17.6,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9720538","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}
Modifying neural activity is a substantial goal in neuroscience that facilitates the understanding of brain functions and the development of medical therapies. Neurobiological models play an essential role, contributing to the understanding of the underlying brain dynamics. In this context, control systems represent a fundamental tool to provide a correct articulation between model stimulus (system inputs) and outcomes (system outputs). However, throughout the literature there is a lack of discussions on neurobiological models, from the formal control perspective. In general, existing control proposals applied to this family of systems, are developed empirically, without theoretical and rigorous framework. Thus, the existing control solutions, present clear and significant limitations. The focus of this work is to survey dynamical neurobiological models that could serve for closed-loop control schemes or for simulation analysis. Consequently, this paper provides a comprehensive guide to discuss and analyze control-oriented neurobiological models. It also provides a potential framework to adequately tackle control problems that could modify the behavior of single neurons or networks. Thus, this study constitutes a key element in the upcoming discussions and studies regarding control methodologies applied to neurobiological systems, to extend the present research and understanding horizon for this field.
{"title":"Dynamical Models in Neuroscience From a Closed-Loop Control Perspective","authors":"Sebastián Martínez;Demián García-Violini;Mariano Belluscio;Joaquín Piriz;Ricardo Sánchez-Peña","doi":"10.1109/RBME.2022.3180559","DOIUrl":"10.1109/RBME.2022.3180559","url":null,"abstract":"Modifying neural activity is a substantial goal in neuroscience that facilitates the understanding of brain functions and the development of medical therapies. Neurobiological models play an essential role, contributing to the understanding of the underlying brain dynamics. In this context, control systems represent a fundamental tool to provide a correct articulation between model stimulus (system inputs) and outcomes (system outputs). However, throughout the literature there is a lack of discussions on neurobiological models, from the formal control perspective. In general, existing control proposals applied to this family of systems, are developed empirically, without theoretical and rigorous framework. Thus, the existing control solutions, present clear and significant limitations. The focus of this work is to survey dynamical neurobiological models that could serve for closed-loop control schemes or for simulation analysis. Consequently, this paper provides a comprehensive guide to discuss and analyze control-oriented neurobiological models. It also provides a potential framework to adequately tackle control problems that could modify the behavior of single neurons or networks. Thus, this study constitutes a key element in the upcoming discussions and studies regarding control methodologies applied to neurobiological systems, to extend the present research and understanding horizon for this field.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"706-721"},"PeriodicalIF":17.6,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9720537","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}
Fetal phonocardiography (fPCG) is receiving attention as it is a promising method for continuous fetal monitoring due to its non-invasive and passive nature. However, it suffers from the interference from various sources, overlapping the desired signal in the time and frequency domains. This paper introduces the state-of-the-art methods used for fPCG signal extraction and processing, as well as means of detection and classification of various features defining fetal health state. It also provides an extensive summary of remaining challenges, along with the practical insights and suggestions for the future research directions.
{"title":"A Review of Recent Advances and Future Developments in Fetal Phonocardiography","authors":"Radana Kahankova;Martina Mikolasova;Rene Jaros;Katerina Barnova;Martina Ladrova;Radek Martinek","doi":"10.1109/RBME.2022.3179633","DOIUrl":"10.1109/RBME.2022.3179633","url":null,"abstract":"Fetal phonocardiography (fPCG) is receiving attention as it is a promising method for continuous fetal monitoring due to its non-invasive and passive nature. However, it suffers from the interference from various sources, overlapping the desired signal in the time and frequency domains. This paper introduces the state-of-the-art methods used for fPCG signal extraction and processing, as well as means of detection and classification of various features defining fetal health state. It also provides an extensive summary of remaining challenges, along with the practical insights and suggestions for the future research directions.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"653-671"},"PeriodicalIF":17.6,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09786823.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9358720","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 : 2022-06-02DOI: 10.1109/RBME.2022.3179742
Antony S. K. Kho;Ean H. Ooi;Ji J. Foo;Ean T. Ooi
Radiofrequency ablation (RFA) combined with saline infusion into tissue is a promising technique to ablate larger tumours. Nevertheless, the application of saline-infused RFA remains at clinical trials due to the contradictory findings as a result of the inconsistencies in experimental procedures. These inconsistencies not only magnify the number of factors to consider during the treatment, but also obscure the understanding of the role of saline in enlarging the coagulation zone. Consequently, this can result in major complications, which includes unwanted thermal damages to adjacent tissues and also incomplete ablation of the tumour. This review aims to identify the key factors of saline responsible for enlarging the coagulation zone during saline-infused RFA, and provide a proper understanding on their effects that is supported with findings from computational studies to ensure a safe and reliable cancer treatment.
{"title":"Saline-Infused Radiofrequency Ablation: A Review on the Key Factors for a Safe and Reliable Tumour Treatment","authors":"Antony S. K. Kho;Ean H. Ooi;Ji J. Foo;Ean T. Ooi","doi":"10.1109/RBME.2022.3179742","DOIUrl":"10.1109/RBME.2022.3179742","url":null,"abstract":"Radiofrequency ablation (RFA) combined with saline infusion into tissue is a promising technique to ablate larger tumours. Nevertheless, the application of saline-infused RFA remains at clinical trials due to the contradictory findings as a result of the inconsistencies in experimental procedures. These inconsistencies not only magnify the number of factors to consider during the treatment, but also obscure the understanding of the role of saline in enlarging the coagulation zone. Consequently, this can result in major complications, which includes unwanted thermal damages to adjacent tissues and also incomplete ablation of the tumour. This review aims to identify the key factors of saline responsible for enlarging the coagulation zone during saline-infused RFA, and provide a proper understanding on their effects that is supported with findings from computational studies to ensure a safe and reliable cancer treatment.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"310-321"},"PeriodicalIF":17.6,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130536826","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}
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