Pub Date : 2021-08-24DOI: 10.1109/RBME.2021.3107372
Shiwangi Mishra;Iman Beheshti;Pritee Khanna
Background: Neuroimage analysis has made it possible to perform various anatomical analyses of the brain regions and helps detect different brain conditions/ disorders. Recently, neuroimaging-driven estimation of brain age is introduced as a robust biomarker for detecting different diseases and health conditions. Objective: To present a comprehensive review of brain age frameworks concerning: i) designing view: an overview of brain age frameworks based on image modality and methods used, and ii) clinical aspect: an overview of the application of brain age frameworks for detection of neurological disorders or health conditions. Methods: PubMed is explored to collect 136 articles from January 2010 to June 2021 using “Brain Age Estimation” and “Brain Imaging,” along with combinations of other radiological terms. Results & Conclusion: The studies presented in this review are evidence of using brain age estimation methods in detecting various brain diseases/conditions. The survey also highlights tools and methods for brain age estimation and addresses some future research directions.
{"title":"A Review of Neuroimaging-Driven Brain Age Estimation for Identification of Brain Disorders and Health Conditions","authors":"Shiwangi Mishra;Iman Beheshti;Pritee Khanna","doi":"10.1109/RBME.2021.3107372","DOIUrl":"10.1109/RBME.2021.3107372","url":null,"abstract":"<italic>Background:</i>\u0000 Neuroimage analysis has made it possible to perform various anatomical analyses of the brain regions and helps detect different brain conditions/ disorders. Recently, neuroimaging-driven estimation of brain age is introduced as a robust biomarker for detecting different diseases and health conditions. \u0000<italic>Objective:</i>\u0000 To present a comprehensive review of brain age frameworks concerning: i) designing view: an overview of brain age frameworks based on image modality and methods used, and ii) clinical aspect: an overview of the application of brain age frameworks for detection of neurological disorders or health conditions. \u0000<italic>Methods:</i>\u0000 PubMed is explored to collect 136 articles from January 2010 to June 2021 using “Brain Age Estimation” and “Brain Imaging,” along with combinations of other radiological terms. \u0000<italic>Results & Conclusion:</i>\u0000 The studies presented in this review are evidence of using brain age estimation methods in detecting various brain diseases/conditions. The survey also highlights tools and methods for brain age estimation and addresses some future research directions.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"371-385"},"PeriodicalIF":17.6,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9713293","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 : 2021-07-07DOI: 10.1109/RBME.2021.3095428
Fergal Henry;Martin Glavin;Edward Jones
Cochlear implant technology successfully restores hearing function to patients with sensory impairment. Although cochlear implant users generally hear well in quiet, they still find noisy conditions very challenging, hence the need to employ noise reduction algorithms in these systems to enhance the user experience . This paper reviews noise reduction algorithms in cochlear implants. Traditionally, such algorithms have been classified as either single- or multiple-channel, depending on the number of microphones they use. This review retains this general classification in looking at recent papers and extends it to reflect recent interest in machine learning techniques. The review concludes with consideration of promising future areas of research.
{"title":"Noise Reduction in Cochlear Implant Signal Processing: A Review and Recent Developments","authors":"Fergal Henry;Martin Glavin;Edward Jones","doi":"10.1109/RBME.2021.3095428","DOIUrl":"10.1109/RBME.2021.3095428","url":null,"abstract":"Cochlear implant technology successfully restores hearing function to patients with sensory impairment. Although cochlear implant users generally hear well in quiet, they still find noisy conditions very challenging, hence the need to employ noise reduction algorithms in these systems to enhance the user experience\u0000<italic>.</i>\u0000 This paper reviews noise reduction algorithms in cochlear implants. Traditionally, such algorithms have been classified as either single- or multiple-channel, depending on the number of microphones they use. This review retains this general classification in looking at recent papers and extends it to reflect recent interest in machine learning techniques. The review concludes with consideration of promising future areas of research.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"319-331"},"PeriodicalIF":17.6,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9358756","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 : 2021-06-29DOI: 10.1109/RBME.2021.3093042
Azam Ahmad Bakir;Amr Al Abed;Nigel H. Lovell;Socrates Dokos
Development of cardiac multiphysics models has progressed significantly over the decades and simulations combining multiple physics interactions have become increasingly common. In this review, we summarise the progress in this field focusing on various approaches of integrating ventricular structures. electrophysiological properties, myocardial mechanics, as well as incorporating blood hemodynamics and the circulatory system. Common coupling approaches are discussed and compared, including the advantages and shortcomings of each. Currently used strategies for patient-specific implementations are highlighted and potential future improvements considered.
{"title":"Multiphysics Computational Modelling of the Cardiac Ventricles","authors":"Azam Ahmad Bakir;Amr Al Abed;Nigel H. Lovell;Socrates Dokos","doi":"10.1109/RBME.2021.3093042","DOIUrl":"10.1109/RBME.2021.3093042","url":null,"abstract":"Development of cardiac multiphysics models has progressed significantly over the decades and simulations combining multiple physics interactions have become increasingly common. In this review, we summarise the progress in this field focusing on various approaches of integrating ventricular structures. electrophysiological properties, myocardial mechanics, as well as incorporating blood hemodynamics and the circulatory system. Common coupling approaches are discussed and compared, including the advantages and shortcomings of each. Currently used strategies for patient-specific implementations are highlighted and potential future improvements considered.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"309-324"},"PeriodicalIF":17.6,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39116893","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 : 2021-06-24DOI: 10.1109/RBME.2021.3092208
Giorgia Fiori;Fabio Fuiano;Andrea Scorza;Silvia Conforto;Salvatore Andrea Sciuto
In recent years, statistical studies highlighted an increasing incidence of cardiovascular diseases (CVD) which reflected on additional costs on the healthcare systems worldwide. Pulse wave velocity (PWV) measurement is commonly considered a CVD predictor factor as well as a marker of Arterial Stiffness (AS) since it is closely related to the mechanical characteristics of the arterial wall. An increase in PWV is due to a more rigid arterial system. Because of the prevalence of the elastic component, in young people the PWV is lower than in the elderly. Nowadays, invasive and non-invasive methods for PWV assessment are employed: there is an increasing attention in the development of non-invasive devices which mostly perform a regional PWV measurement (over a long arterial portion) rather than local (over a short arterial portion). The accepted gold-standard for non-invasive AS measurement is the carotid-femoral PWV used to evaluate the arterial damage, the corresponding cardiovascular risk and to adapt the proper therapy. This review article considers the main commercially available devices underlining their operating principles in terms of sensors, execution mode, pulse waveforms acquired, site of measurement, distance and time estimation methods, as well as their main limitations in clinical practice.
{"title":"Non-Invasive Methods for PWV Measurement in Blood Vessel Stiffness Assessment","authors":"Giorgia Fiori;Fabio Fuiano;Andrea Scorza;Silvia Conforto;Salvatore Andrea Sciuto","doi":"10.1109/RBME.2021.3092208","DOIUrl":"10.1109/RBME.2021.3092208","url":null,"abstract":"In recent years, statistical studies highlighted an increasing incidence of cardiovascular diseases (CVD) which reflected on additional costs on the healthcare systems worldwide. Pulse wave velocity (PWV) measurement is commonly considered a CVD predictor factor as well as a marker of Arterial Stiffness (AS) since it is closely related to the mechanical characteristics of the arterial wall. An increase in PWV is due to a more rigid arterial system. Because of the prevalence of the elastic component, in young people the PWV is lower than in the elderly. Nowadays, invasive and non-invasive methods for PWV assessment are employed: there is an increasing attention in the development of non-invasive devices which mostly perform a regional PWV measurement (over a long arterial portion) rather than local (over a short arterial portion). The accepted gold-standard for non-invasive AS measurement is the carotid-femoral PWV used to evaluate the arterial damage, the corresponding cardiovascular risk and to adapt the proper therapy. This review article considers the main commercially available devices underlining their operating principles in terms of sensors, execution mode, pulse waveforms acquired, site of measurement, distance and time estimation methods, as well as their main limitations in clinical practice.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"169-183"},"PeriodicalIF":17.6,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39035216","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 : 2021-06-24DOI: 10.1109/RBME.2021.3092197
Ryan Scott;Wei Jiang;M. Jamal Deen
Time-to-digital converters (TDCs) are high-performance mixed-signal circuits capable of timestamping events with sub-gate delay resolution. As a result of their high-performance, in recent years TDCs were integrated in complementary metal-oxide-semiconductor (CMOS) technology with highly sensitive photodetectors known as single-photon avalanche diodes (SPADs), to form digital silicon photomultipliers (dSiPMs) and SPAD imagers. Time-resolved SPAD-based sensors are capable of detecting the absorption of a single photon and timestamping it with picosecond resolution. As such, SPAD-based sensors are very useful in the field of biomedical imaging, using time-of-flight (ToF) information to produce data that can be used to reconstruct high-quality biological images. Additionally, the capability of integration in standard CMOS technologies, allows SPAD-based sensors to provide high-performance, while maintaining low cost. In this paper, we present an overview of fundamental TDC principles, and an analysis of state-of-the-art TDCs. Furthermore, the integration of TDCs into dSiPMs and SPAD imagers will be discussed, with an analysis of the current results of TDCs in different biomedical imaging applications. Finally, several important research challenges for TDCs in biomedical imaging applications are presented.
{"title":"CMOS Time-to-Digital Converters for Biomedical Imaging Applications","authors":"Ryan Scott;Wei Jiang;M. Jamal Deen","doi":"10.1109/RBME.2021.3092197","DOIUrl":"10.1109/RBME.2021.3092197","url":null,"abstract":"Time-to-digital converters (TDCs) are high-performance mixed-signal circuits capable of timestamping events with sub-gate delay resolution. As a result of their high-performance, in recent years TDCs were integrated in complementary metal-oxide-semiconductor (CMOS) technology with highly sensitive photodetectors known as single-photon avalanche diodes (SPADs), to form digital silicon photomultipliers (dSiPMs) and SPAD imagers. Time-resolved SPAD-based sensors are capable of detecting the absorption of a single photon and timestamping it with picosecond resolution. As such, SPAD-based sensors are very useful in the field of biomedical imaging, using time-of-flight (ToF) information to produce data that can be used to reconstruct high-quality biological images. Additionally, the capability of integration in standard CMOS technologies, allows SPAD-based sensors to provide high-performance, while maintaining low cost. In this paper, we present an overview of fundamental TDC principles, and an analysis of state-of-the-art TDCs. Furthermore, the integration of TDCs into dSiPMs and SPAD imagers will be discussed, with an analysis of the current results of TDCs in different biomedical imaging applications. Finally, several important research challenges for TDCs in biomedical imaging applications are presented.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"627-652"},"PeriodicalIF":17.6,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9370452","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 : 2021-06-01DOI: 10.1109/RBME.2021.3085484
Roberta Piazza;M. Carbone;R. N. Berchiolli;V. Ferrari;M. Ferrari;S. Condino
In situ fenestration of stent-graft represents a potential option for the treatment of aortic diseases in patients unsuitable for standard endovascular repair. The best fenestration strategy to restore perfusion of collateral vessels after their coverage by an endograft depends mainly on the anatomical area. Several tools are employed as fenestration devices, including needles, radiofrequency probes, and laser systems, used in conjunction with other instrumentation to provide enough support and stability during the procedure. In this systematic review, the approaches to reach the correct fenestration site both in human, animal, and in in vitro environments are described and discussed, highlighting advantages and limitations. Both commercial and dedicated solutions for the intraoperative modification of the fabric material are reported as well. The clinical interest in this procedure has so far encouraged researchers to develop and refine both methods and tools to solve the current limitations of this technique, intending to extend the indications for endovascular treatment to a broader range of patients.
{"title":"A Systematic Review on Methods and Tools for the In Situ Fenestration of Aortic Stent-Graft","authors":"Roberta Piazza;M. Carbone;R. N. Berchiolli;V. Ferrari;M. Ferrari;S. Condino","doi":"10.1109/RBME.2021.3085484","DOIUrl":"10.1109/RBME.2021.3085484","url":null,"abstract":"In situ fenestration of stent-graft represents a potential option for the treatment of aortic diseases in patients unsuitable for standard endovascular repair. The best fenestration strategy to restore perfusion of collateral vessels after their coverage by an endograft depends mainly on the anatomical area. Several tools are employed as fenestration devices, including needles, radiofrequency probes, and laser systems, used in conjunction with other instrumentation to provide enough support and stability during the procedure. In this systematic review, the approaches to reach the correct fenestration site both in human, animal, and in in vitro environments are described and discussed, highlighting advantages and limitations. Both commercial and dedicated solutions for the intraoperative modification of the fabric material are reported as well. The clinical interest in this procedure has so far encouraged researchers to develop and refine both methods and tools to solve the current limitations of this technique, intending to extend the indications for endovascular treatment to a broader range of patients.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"348-356"},"PeriodicalIF":17.6,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2021.3085484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9359417","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 : 2021-04-26DOI: 10.1109/RBME.2021.3075500
Sushmita Mitra
A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with genomic information. It encompasses mining tumor radioimages employing deep learning for the automated extraction of relevant features from the segmented volume of interest (VOI). Gene expression values from surgically extracted tumor tissues are often simultaneously analyzed to determine patient specific features. Association between genomic and radiomic features are also explored in some cases to determine the imaging surrogates. Deep learning and transfer learning are typically exploited for efficient knowledge discovery and decision-making. Some studies on survival prediction ensemble learning and interactive learning are also included. The literature mainly focuses on magnetic resonance imaging (MRI) data of the brain for learning and validation and generally involves the NIH TCIA and TCGA repositories as well as the BraTS Challenge databases.
{"title":"Deep Learning With Radiogenomics Towards Personalized Management of Gliomas","authors":"Sushmita Mitra","doi":"10.1109/RBME.2021.3075500","DOIUrl":"10.1109/RBME.2021.3075500","url":null,"abstract":"A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with genomic information. It encompasses mining tumor radioimages employing deep learning for the automated extraction of relevant features from the segmented volume of interest (VOI). Gene expression values from surgically extracted tumor tissues are often simultaneously analyzed to determine patient specific features. Association between genomic and radiomic features are also explored in some cases to determine the imaging surrogates. Deep learning and transfer learning are typically exploited for efficient knowledge discovery and decision-making. Some studies on survival prediction ensemble learning and interactive learning are also included. The literature mainly focuses on magnetic resonance imaging (MRI) data of the brain for learning and validation and generally involves the NIH TCIA and TCGA repositories as well as the BraTS Challenge databases.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"579-593"},"PeriodicalIF":17.6,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2021.3075500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9422802","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}
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.
{"title":"A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases","authors":"Wei Jiang;Sumit Majumder;Samarth Kumar;Sophini Subramaniam;Xiaohe Li;Ridha Khedri;Tapas Mondal;Mansour Abolghasemian;Imran Satia;M. Jamal Deen","doi":"10.1109/RBME.2021.3069815","DOIUrl":"10.1109/RBME.2021.3069815","url":null,"abstract":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"61-84"},"PeriodicalIF":17.6,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2021.3069815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25532097","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 : 2021-03-26DOI: 10.1109/RBME.2021.3069213
J. Christopher Clement;VijayaKumar Ponnusamy;K.C. Sriharipriya;R. Nandakumar
COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.
{"title":"A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis","authors":"J. Christopher Clement;VijayaKumar Ponnusamy;K.C. Sriharipriya;R. Nandakumar","doi":"10.1109/RBME.2021.3069213","DOIUrl":"10.1109/RBME.2021.3069213","url":null,"abstract":"COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"325-340"},"PeriodicalIF":17.6,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2021.3069213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25519227","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 : 2021-03-18DOI: 10.1109/RBME.2021.3081180
Jingjing Luo;Zhengrong Yan;Shijie Guo;Wei Chen
Atherosclerosis screening helps the medical model transform from therapeutic medicine to preventive medicine by assessing degree of atherosclerosis prior to the occurrence of fatal vascular events. Pervasive screening emphasizes atherosclerotic monitoring with easy access, quick process, and advanced computing. In this work, we introduced five cutting-edge pervasive technologies including imaging photoplethysmography (iPPG), laser Doppler, radio frequency (RF), thermal imaging (TI), optical fiber sensing and piezoelectric sensor. IPPG measures physiological parameters by using video images that record the subtle skin color changes consistent with cardiac-synchronous blood volume changes in subcutaneous arteries and capillaries. Laser Doppler obtained the information on blood flow by analyzing the spectral components of backscattered light from the illuminated tissues’ surface. RF is based on Doppler shift caused by the periodic movement of the chest wall induced by respiration and heartbeat. TI measures vital signs by detecting electromagnetic radiation emitted by blood flow. The working principle of optical fiber sensor is to detect the change of light properties caused by the interaction between the measured physiological parameter and the entering light. Piezoelectric sensors are based on the piezoelectric effect of dielectrics. All these pervasive technologies are noninvasive, mobile, and can detect physiological parameters related to atherosclerosis screening.
{"title":"Recent Advances in Atherosclerotic Disease Screening Using Pervasive Healthcare","authors":"Jingjing Luo;Zhengrong Yan;Shijie Guo;Wei Chen","doi":"10.1109/RBME.2021.3081180","DOIUrl":"10.1109/RBME.2021.3081180","url":null,"abstract":"Atherosclerosis screening helps the medical model transform from therapeutic medicine to preventive medicine by assessing degree of atherosclerosis prior to the occurrence of fatal vascular events. Pervasive screening emphasizes atherosclerotic monitoring with easy access, quick process, and advanced computing. In this work, we introduced five cutting-edge pervasive technologies including imaging photoplethysmography (iPPG), laser Doppler, radio frequency (RF), thermal imaging (TI), optical fiber sensing and piezoelectric sensor. IPPG measures physiological parameters by using video images that record the subtle skin color changes consistent with cardiac-synchronous blood volume changes in subcutaneous arteries and capillaries. Laser Doppler obtained the information on blood flow by analyzing the spectral components of backscattered light from the illuminated tissues’ surface. RF is based on Doppler shift caused by the periodic movement of the chest wall induced by respiration and heartbeat. TI measures vital signs by detecting electromagnetic radiation emitted by blood flow. The working principle of optical fiber sensor is to detect the change of light properties caused by the interaction between the measured physiological parameter and the entering light. Piezoelectric sensors are based on the piezoelectric effect of dielectrics. All these pervasive technologies are noninvasive, mobile, and can detect physiological parameters related to atherosclerosis screening.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"293-308"},"PeriodicalIF":17.6,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2021.3081180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38912242","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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