Pub Date : 2022-11-08DOI: 10.1109/RBME.2022.3220636
Leif Sörnmo;Raquel Bailón;Pablo Laguna
The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time–frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.
{"title":"Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors","authors":"Leif Sörnmo;Raquel Bailón;Pablo Laguna","doi":"10.1109/RBME.2022.3220636","DOIUrl":"10.1109/RBME.2022.3220636","url":null,"abstract":"The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time–frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"322-341"},"PeriodicalIF":17.6,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40451706","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 : 2022-11-08DOI: 10.1109/RBME.2022.3220505
Alireza Golgouneh;Lucy E. Dunne
Body compression through a garment or inflatable pneumatic mechanism has various applications in aesthetic, athletic, robotics, haptics, astronautics, and especially medical fields for treatment of various disorders such as varicose veins, lymphedema, deep vein thrombosis, and orthostatic intolerance. Traditionally, compression has been done through under-sized (e.g. elastic) or size-adjustable (e.g. inflatable) compression garments. Such systems are designed to apply substantially uniform pressure on the body. However, due to reasons such as anatomical variations and body posture change, different levels of compression may be applied to the body. Further, a high level of discomfort and non-compliance is reported among patients due to donning difficulties. Therefore, there have been some efforts to make compression garments smart by employing advanced functional soft materials and actuators (such as Shape Memory Alloy (SMA), Shape Memory Polymer (SMP), Electroactive polymer (EAP), etc.) as well as soft force-pressure sensors so that the compression level could be controlled and regulated for each person or specific tasks. However, despite these advances, there are still challenges to accurately controlling the on-body compression level that are mainly due to the inherent characteristics of the soft actuators or sensors and the sophisticated human body conditions. In this paper, we will first investigate the soft actuators and sensors that have the potential to be used for on-body compression applications. Then, integrated soft sensing-actuation systems for interfacial compression purposes are studied. Finally, the challenges that might be associated with this work are introduced.
{"title":"A Review in On-Body Compression Using Soft Actuators and Sensors: Applications, Mechanisms, and Challenges","authors":"Alireza Golgouneh;Lucy E. Dunne","doi":"10.1109/RBME.2022.3220505","DOIUrl":"10.1109/RBME.2022.3220505","url":null,"abstract":"Body compression through a garment or inflatable pneumatic mechanism has various applications in aesthetic, athletic, robotics, haptics, astronautics, and especially medical fields for treatment of various disorders such as varicose veins, lymphedema, deep vein thrombosis, and orthostatic intolerance. Traditionally, compression has been done through under-sized (e.g. elastic) or size-adjustable (e.g. inflatable) compression garments. Such systems are designed to apply substantially uniform pressure on the body. However, due to reasons such as anatomical variations and body posture change, different levels of compression may be applied to the body. Further, a high level of discomfort and non-compliance is reported among patients due to donning difficulties. Therefore, there have been some efforts to make compression garments smart by employing advanced functional soft materials and actuators (such as Shape Memory Alloy (SMA), Shape Memory Polymer (SMP), Electroactive polymer (EAP), etc.) as well as soft force-pressure sensors so that the compression level could be controlled and regulated for each person or specific tasks. However, despite these advances, there are still challenges to accurately controlling the on-body compression level that are mainly due to the inherent characteristics of the soft actuators or sensors and the sophisticated human body conditions. In this paper, we will first investigate the soft actuators and sensors that have the potential to be used for on-body compression applications. Then, integrated soft sensing-actuation systems for interfacial compression purposes are studied. Finally, the challenges that might be associated with this work are introduced.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"166-179"},"PeriodicalIF":17.6,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40451707","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 : 2022-11-04DOI: 10.1109/RBME.2022.3219433
Maggie Ezzat Gaber Gendy;Mehmet Rasit Yuce
Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.
{"title":"Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic","authors":"Maggie Ezzat Gaber Gendy;Mehmet Rasit Yuce","doi":"10.1109/RBME.2022.3219433","DOIUrl":"10.1109/RBME.2022.3219433","url":null,"abstract":"Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"38-52"},"PeriodicalIF":17.6,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9359277","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 : 2022-10-27DOI: 10.1109/RBME.2022.3217486
David Choy Buentello;Mariana García-Corral;Grissel Trujillo-de Santiago;Mario Moisés Alvarez
Neuron-on-chip (NoC) systems—microfluidic devices in which neurons are cultured—have become a promising alternative to replace or minimize the use of animal models and have greatly facilitated in vitro research. Here, we review and discuss current developments in neuron-on-chip platforms, with a particular emphasis on existing biological models, culturing techniques, biomaterials, and topologies. We also discuss how the architecture, flow, and gradients affect neuronal growth, differentiation, and development. Finally, we discuss some of the most recent applications of NoCs in fundamental research (i.e., studies on the effects of electrical, mechanical/topological, or chemical stimuli) and in disease modeling.
{"title":"Neuron(s)-on-a-Chip: A Review of the Design and Use of Microfluidic Systems for Neural Tissue Culture","authors":"David Choy Buentello;Mariana García-Corral;Grissel Trujillo-de Santiago;Mario Moisés Alvarez","doi":"10.1109/RBME.2022.3217486","DOIUrl":"10.1109/RBME.2022.3217486","url":null,"abstract":"Neuron-on-chip (NoC) systems—microfluidic devices in which neurons are cultured—have become a promising alternative to replace or minimize the use of animal models and have greatly facilitated in vitro research. Here, we review and discuss current developments in neuron-on-chip platforms, with a particular emphasis on existing biological models, culturing techniques, biomaterials, and topologies. We also discuss how the architecture, flow, and gradients affect neuronal growth, differentiation, and development. Finally, we discuss some of the most recent applications of NoCs in fundamental research (i.e., studies on the effects of electrical, mechanical/topological, or chemical stimuli) and in disease modeling.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"243-263"},"PeriodicalIF":17.6,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9255690","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 : 2022-10-21DOI: 10.1109/RBME.2022.3216531
Monica Isgut;Logan Gloster;Katherine Choi;Janani Venugopalan;May D. Wang
At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.
{"title":"Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era","authors":"Monica Isgut;Logan Gloster;Katherine Choi;Janani Venugopalan;May D. Wang","doi":"10.1109/RBME.2022.3216531","DOIUrl":"10.1109/RBME.2022.3216531","url":null,"abstract":"At the beginning of the COVID-19 pandemic, there was significant hype about the potential impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or surveillance. However, AI tools have not yet been widely successful. One of the key reason is the COVID-19 pandemic has demanded faster real-time development of AI-driven clinical and health support tools, including rapid data collection, algorithm development, validation, and deployment. However, there was not enough time for proper data quality control. Learning from the hard lessons in COVID-19, we summarize the important health data quality challenges during COVID-19 pandemic such as lack of data standardization, missing data, tabulation errors, and noise and artifact. Then we conduct a systematic investigation of computational methods that address these issues, including emerging novel advanced AI data quality control methods that achieve better data quality outcomes and, in some cases, simplify or automate the data cleaning process. We hope this article can assist healthcare community to improve health data quality going forward with novel AI development.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"53-69"},"PeriodicalIF":17.6,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09926151.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9728920","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 : 2022-10-19DOI: 10.1109/RBME.2022.3215678
Sabrina Schoenborn;Selene Pirola;Maria A. Woodruff;Mark C. Allenby
Cardiovascular disease (CVD) is the leading cause of mortality worldwide and its incidence is rising due to an aging population. The development and progression of CVD is directly linked to adverse vascular hemodynamics and biomechanics, whose in-vivo measurement remains challenging but can be simulated numerically and experimentally. The ability to evaluate these parameters in patient-specific CVD cases is crucial to better predict future disease progression, risk of adverse events, and treatment efficacy. While significant progress has been made toward patient-specific hemodynamic simulations, blood vessels are often assumed to be rigid, which does not consider the compliant mechanical properties of vessels whose malfunction is implicated in disease. In an effort to simulate the biomechanics of flexible vessels, fluid-structure interaction (FSI) simulations have emerged as promising tools for the characterization of hemodynamics within patient-specific cardiovascular anatomies. Since FSI simulations combine the blood's fluid domain with the arterial structural domain, they pose novel challenges for their experimental validation. This paper reviews the scientific work related to FSI simulations for patient-specific arterial geometries and the current standard of FSI model validation including the use of compliant arterial phantoms, which offer novel potential for the experimental validation of FSI results.
{"title":"Fluid-Structure Interaction Within Models of Patient-Specific Arteries: Computational Simulations and Experimental Validations","authors":"Sabrina Schoenborn;Selene Pirola;Maria A. Woodruff;Mark C. Allenby","doi":"10.1109/RBME.2022.3215678","DOIUrl":"10.1109/RBME.2022.3215678","url":null,"abstract":"Cardiovascular disease (CVD) is the leading cause of mortality worldwide and its incidence is rising due to an aging population. The development and progression of CVD is directly linked to adverse vascular hemodynamics and biomechanics, whose in-vivo measurement remains challenging but can be simulated numerically and experimentally. The ability to evaluate these parameters in patient-specific CVD cases is crucial to better predict future disease progression, risk of adverse events, and treatment efficacy. While significant progress has been made toward patient-specific hemodynamic simulations, blood vessels are often assumed to be rigid, which does not consider the compliant mechanical properties of vessels whose malfunction is implicated in disease. In an effort to simulate the biomechanics of flexible vessels, fluid-structure interaction (FSI) simulations have emerged as promising tools for the characterization of hemodynamics within patient-specific cardiovascular anatomies. Since FSI simulations combine the blood's fluid domain with the arterial structural domain, they pose novel challenges for their experimental validation. This paper reviews the scientific work related to FSI simulations for patient-specific arterial geometries and the current standard of FSI model validation including the use of compliant arterial phantoms, which offer novel potential for the experimental validation of FSI results.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"280-296"},"PeriodicalIF":17.6,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40341837","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 : 2022-10-10DOI: 10.1109/RBME.2022.3212695
Kareeb Hasan;Malikeh P. Ebrahim;Hongqiang Xu;Mehmet R. Yuce
Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately.
{"title":"Analysis of Spectral Estimation Algorithms for Accurate Heart Rate and Respiration Rate Estimation Using an Ultra-Wideband Radar Sensor","authors":"Kareeb Hasan;Malikeh P. Ebrahim;Hongqiang Xu;Mehmet R. Yuce","doi":"10.1109/RBME.2022.3212695","DOIUrl":"10.1109/RBME.2022.3212695","url":null,"abstract":"Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"297-309"},"PeriodicalIF":17.6,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33498023","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 : 2022-10-05DOI: 10.1109/RBME.2022.3212038
Shobhit K. Patel;Jaymit Surve;Juveriya Parmar;Kawsar Ahmed;Francis M. Bui;Fahad Ahmed Al-Zahrani
This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers.
{"title":"Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses","authors":"Shobhit K. Patel;Jaymit Surve;Juveriya Parmar;Kawsar Ahmed;Francis M. Bui;Fahad Ahmed Al-Zahrani","doi":"10.1109/RBME.2022.3212038","DOIUrl":"10.1109/RBME.2022.3212038","url":null,"abstract":"This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"22-37"},"PeriodicalIF":17.6,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9911770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9351776","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 : 2022-09-29DOI: 10.1109/RBME.2022.3210598
Tom Greig;Russel Torah;Kai Yang
Ulcers and chronic wounds are a large and expensive problem, costing billions of pounds a year and affecting millions of people. Electrical stimulation has been known to have a positive effect on wound healing since the 1960s and this has been confirmed in numerous studies, reducing the time to heal, and the incidence of adverse events such as infections. However, because each study used different parameters for the treatment, inclusion criteria and metrics for quantifying the success, it is currently hard to combine them statistically and gain a true picture of its efficacy. As such, electrical stimulation has not been universally adopted as a recommended treatment for various types of wound. This paper summarises the biological basis for electrical simulation treatment and reviews the clinical evidence for its effectiveness. Notable is the lack of research focused on the electrodes used to deliver electrostimulation treatment. However, a significant amount of work has been conducted on electrodes for other medical applications in the field of e-textiles. This e-textile work is reviewed with a focus on its potential in electrostimulation and proposals are made for future developments to improve future studies and applications for wound healing via electrical stimulation.
{"title":"Electrical Stimulation for Wound Healing: Opportunities for E-Textiles","authors":"Tom Greig;Russel Torah;Kai Yang","doi":"10.1109/RBME.2022.3210598","DOIUrl":"10.1109/RBME.2022.3210598","url":null,"abstract":"Ulcers and chronic wounds are a large and expensive problem, costing billions of pounds a year and affecting millions of people. Electrical stimulation has been known to have a positive effect on wound healing since the 1960s and this has been confirmed in numerous studies, reducing the time to heal, and the incidence of adverse events such as infections. However, because each study used different parameters for the treatment, inclusion criteria and metrics for quantifying the success, it is currently hard to combine them statistically and gain a true picture of its efficacy. As such, electrical stimulation has not been universally adopted as a recommended treatment for various types of wound. This paper summarises the biological basis for electrical simulation treatment and reviews the clinical evidence for its effectiveness. Notable is the lack of research focused on the electrodes used to deliver electrostimulation treatment. However, a significant amount of work has been conducted on electrodes for other medical applications in the field of e-textiles. This e-textile work is reviewed with a focus on its potential in electrostimulation and proposals are made for future developments to improve future studies and applications for wound healing via electrical stimulation.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"264-279"},"PeriodicalIF":17.6,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40379722","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 : 2022-09-28DOI: 10.1109/RBME.2022.3210270
Binggui Zhou;Guanghua Yang;Zheng Shi;Shaodan Ma
Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work, we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We first elaborate on different NLP approaches and the NLP pipeline for smart healthcare from the technical point of view. Then, in the context of smart healthcare applications employing NLP techniques, we introduce representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss two specific medical issues, i.e., the coronavirus disease 2019 (COVID-19) pandemic and mental health, in which NLP-driven smart healthcare plays an important role. Finally, we discuss the limitations of current works and identify the directions for future works.
{"title":"Natural Language Processing for Smart Healthcare","authors":"Binggui Zhou;Guanghua Yang;Zheng Shi;Shaodan Ma","doi":"10.1109/RBME.2022.3210270","DOIUrl":"10.1109/RBME.2022.3210270","url":null,"abstract":"Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work, we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We first elaborate on different NLP approaches and the NLP pipeline for smart healthcare from the technical point of view. Then, in the context of smart healthcare applications employing NLP techniques, we introduce representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss two specific medical issues, i.e., the coronavirus disease 2019 (COVID-19) pandemic and mental health, in which NLP-driven smart healthcare plays an important role. Finally, we discuss the limitations of current works and identify the directions for future works.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"17 ","pages":"4-18"},"PeriodicalIF":17.6,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40380475","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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