IEEE Reviews in Biomedical Engineering最新文献_第10页

Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review 基于人工智能的听诊波形和示波波形血压估计方法综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-11-25 DOI: 10.1109/RBME.2020.3040715

Ahmadreza Argha;Branko G. Celler;Nigel H. Lovell

{"title":"Artificial Intelligence Based Blood Pressure Estimation From Auscultatory and Oscillometric Waveforms: A Methodological Review","authors":"Ahmadreza Argha;Branko G. Celler;Nigel H. Lovell","doi":"10.1109/RBME.2020.3040715","DOIUrl":"10.1109/RBME.2020.3040715","url":null,"abstract":"Cardiovascular disease is known as the number one cause of death globally, with elevated blood pressure (BP) being the single largest risk factor. Hence, BP is an important physiological parameter used as an indicator of cardiovascular health. The use of automated non-invasive blood pressure (NIBP) measurement devices is growing, as they can be used without expertise and BP measurement can be performed by patients at home. Non-invasive cuff-based monitoring is the dominant method for BP measurement. While the oscillometric technique is most common, some automated NIBP measurement methods have been developed based on the auscultatory technique. By utilizing (relatively) large BP data annotated by experts, models can be trained using machine learning and statistical concepts to develop novel NIBP estimation algorithms. Amongst artificial intelligence (AI) techniques, deep learning has received increasing attention in different fields due to its strength in data classification and feature extraction problems. This paper reviews AI-based BP estimation methods with a focus on recent advances in deep learning-based approaches within the field. Various architectures and methodologies proposed todate are discussed to clarify their strengths and weaknesses. Based on the literature reviewed, deep learning brings plausible benefits to the field of BP estimation. We also discuss some limitations which can hinder the widespread adoption of deep learning in the field and suggest frameworks to overcome these challenges.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"152-168"},"PeriodicalIF":17.6,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2020.3040715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38642389","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}

引用次数: 9

Respiratory Monitoring: Current State of the Art and Future Roads 呼吸监测：当前的技术状态和未来的道路

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-11-06 DOI: 10.1109/RBME.2020.3036330

Ian Costanzo;Devdip Sen;Lawrence Rhein;Ulkuhan Guler

{"title":"Respiratory Monitoring: Current State of the Art and Future Roads","authors":"Ian Costanzo;Devdip Sen;Lawrence Rhein;Ulkuhan Guler","doi":"10.1109/RBME.2020.3036330","DOIUrl":"10.1109/RBME.2020.3036330","url":null,"abstract":"In this article, we present current methodologies, available technologies, and demands for monitoring various respiratory parameters. We discuss the importance of noninvasive techniques for remote and continuous monitoring and challenges involved in the current “smart and connected health” era. We conducted an extensive literature review on the medical significance of monitoring respiratory vital parameters, along with the current methods and solutions with their respective advantages and disadvantages. We discuss the challenges of developing a noninvasive, wearable, wireless system that continuously monitors respiration parameters and opportunities in the field and then determine the requirements of a state-of-the-art system. Noninvasive techniques provide a significant amount of medical information for a continuous patient monitoring system. Contact methods offer more advantages than non-contact methods; however, reducing the size and power of contact methods is critical for enabling a wearable, wireless medical monitoring system. Continuous and accurate remote monitoring, along with other physiological data, can help caregivers improve the quality of care and allow patients greater freedom outside the hospital. Such monitoring systems could lead to highly tailored treatment plans, shorten patient stays at medical facilities, and reduce the cost of treatment.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"103-121"},"PeriodicalIF":17.6,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38669282","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}

引用次数: 24

Nanoparticle Enhanced Optical Biosensing Technologies for Prostate Specific Antigen Biomarker Detection 纳米粒子增强光学生物传感技术检测前列腺特异性抗原生物标志物

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-11-02 DOI: 10.1109/RBME.2020.3035273

Ahmad Usman

{"title":"Nanoparticle Enhanced Optical Biosensing Technologies for Prostate Specific Antigen Biomarker Detection","authors":"Ahmad Usman","doi":"10.1109/RBME.2020.3035273","DOIUrl":"10.1109/RBME.2020.3035273","url":null,"abstract":"Prostate Cancer (PCa) is one of the deadliest forms of Cancer among men. Early screening process for PCa is primarily conducted with the help of a FDA approved biomarker known as Prostate Specific Antigen (PSA). The PSA-based screening is challenged with the inability to differentiate between the cancerous PSA and Benign Prostatic Hyperplasia (BPH), resulting in high rates of false-positives. Optical techniques such as optical absorbance, scattering, surface plasmon resonance (SPR), and fluorescence have been extensively employed for Cancer diagnostic applications. One of the most important diagnostic applications involves utilization of nanoparticles (NPs) for highly specific, sensitive, rapid, multiplexed, and high performance Cancer detection and quantification. The incorporation of NPs with these optical biosensing techniques allow realization of low cost, point-of-care, highly sensitive, and specific early cancer detection technologies, especially for PCa. In this work, the current state-of-the-art, challenges, and efforts made by the researchers for realization of low cost, point-of-care (POC), highly sensitive, and specific NP enhanced optical biosensing technologies for PCa detection using PSA biomarker are discussed and analyzed.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"122-137"},"PeriodicalIF":17.6,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2020.3035273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38660480","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}

引用次数: 2

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey 诊断睡眠呼吸障碍的生物医学传感器、技术和算法综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-10-26 DOI: 10.1109/RBME.2020.3033930

Sahar Ahmadzadeh;Jikui Luo;Richard Wiffen

{"title":"Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey","authors":"Sahar Ahmadzadeh;Jikui Luo;Richard Wiffen","doi":"10.1109/RBME.2020.3033930","DOIUrl":"10.1109/RBME.2020.3033930","url":null,"abstract":"This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient’s physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"4-22"},"PeriodicalIF":17.6,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2020.3033930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38530014","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}

引用次数: 17

Uterus Modeling From Cell to Organ Level: Towards Better Understanding of Physiological Basis of Uterine Activity 从细胞到器官的子宫建模：更好地理解子宫活动的生理基础

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-09-11 DOI: 10.1109/RBME.2020.3023535

Yuhang Xu;Haipeng Liu;Dongmei Hao;Michael Taggart;Dingchang Zheng

{"title":"Uterus Modeling From Cell to Organ Level: Towards Better Understanding of Physiological Basis of Uterine Activity","authors":"Yuhang Xu;Haipeng Liu;Dongmei Hao;Michael Taggart;Dingchang Zheng","doi":"10.1109/RBME.2020.3023535","DOIUrl":"10.1109/RBME.2020.3023535","url":null,"abstract":"The relatively limited understanding of the physiology of uterine activation prevents us from achieving optimal clinical outcomes for managing serious pregnancy disorders such as preterm birth or uterine dystocia. There is increasing awareness that multi-scale computational modeling of the uterus is a promising approach for providing a qualitative and quantitative description of uterine physiology. The overarching objective of such approach is to coalesce previously fragmentary information into a predictive and testable model of uterine activity that, in turn, informs the development of new diagnostic and therapeutic approaches to these pressing clinical problems. This article assesses current progress towards this goal. We summarize the electrophysiological basis of uterine activation as presently understood and review recent research approaches to uterine modeling at different scales from single cell to tissue, whole organ and organism with particular focus on transformative data in the last decade. We describe the positives and limitations of these approaches, thereby identifying key gaps in our knowledge on which to focus, in parallel, future computational and biological research efforts.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"341-353"},"PeriodicalIF":17.6,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2020.3023535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38367846","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}

引用次数: 2

Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review 基于可穿戴传感器的手语识别综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-08-26 DOI: 10.1109/RBME.2020.3019769

Karly Kudrinko;Emile Flavin;Xiaodan Zhu;Qingguo Li

引用次数: 46

The Promise of Clinical Decision Support Systems Targetting Low-Resource Settings 针对低资源环境的临床决策支持系统的前景

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-08-19 DOI: 10.1109/RBME.2020.3017868

Dani Kiyasseh;Tingting Zhu;David Clifton

{"title":"The Promise of Clinical Decision Support Systems Targetting Low-Resource Settings","authors":"Dani Kiyasseh;Tingting Zhu;David Clifton","doi":"10.1109/RBME.2020.3017868","DOIUrl":"10.1109/RBME.2020.3017868","url":null,"abstract":"Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage of high-quality medical expertise and infrastructure. Together, these phenomena lead to over-burdened healthcare systems that under-serve the needs of the community. Alleviating this burden can be undertaken by the introduction of clinical decision support systems (CDSSs); systems that support stakeholders (ranging from physicians to patients) within the clinical setting in their day-to-day activities. Such systems, which have proven to be effective in the developed world, remain to be under-explored in low-resource settings. This review attempts to summarize the research focused on clinical decision support systems that either target stakeholders within low-resource clinical settings or diseases commonly found in such environments. When categorizing our findings according to disease applications, we find that CDSSs are predominantly focused on dealing with bacterial infections and maternal care, do not leverage deep learning, and have not been evaluated prospectively. Together, these highlight the need for increased research in this domain in order to impact a diverse set of medical conditions and ultimately improve patient outcomes.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"354-371"},"PeriodicalIF":17.6,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2020.3017868","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38279513","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}

引用次数: 2

Secure and Robust Machine Learning for Healthcare: A Survey 用于医疗保健的安全和稳健的机器学习：一项调查

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-07-31 DOI: 10.1109/RBME.2020.3013489

Adnan Qayyum;Junaid Qadir;Muhammad Bilal;Ala Al-Fuqaha

引用次数: 228

Simulation of Blood as Fluid: A Review From Rheological Aspects 血液作为流体的模拟：从流变学角度综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-07-22 DOI: 10.1109/RBME.2020.3011182

Vikas Kannojiya;Arup Kumar Das;Prasanta Kumar Das

{"title":"Simulation of Blood as Fluid: A Review From Rheological Aspects","authors":"Vikas Kannojiya;Arup Kumar Das;Prasanta Kumar Das","doi":"10.1109/RBME.2020.3011182","DOIUrl":"10.1109/RBME.2020.3011182","url":null,"abstract":"Blood flow in the human vascular system is a complex to understand example of fluid dynamics in a closed conduit. Any irregularities in the hemodynamics may lead to lethal cardiovascular disease like heart attack, heart failure and ischemia. Numerical simulation of hemodynamics in the blood vessel can facilitate a thorough understanding of blood flow and its interaction with the adjacent vessel wall. A good simulation approach for blood flow can be helpful in early prediction and diagnosis of the mentioned disease. The simulation outcomes may also provide decision support for surgical planning and medical implants. This study reports an extensive review of various approaches adopted to analyze the influence of blood rheological characteristics in a different class of blood vessels. In particular, emphasis was given on the identification of best possible rheological model to effectively solve the hemodynamics inside different blood vessels. The performance capability of different rheological models was discussed for different classes and conditions of vessels and the best/poor performing models are listed out. The Carreau, Casson and generalized power-law models were appeared to be superior for solving the blood flow at all shear rates. In contrast, power law, Walburn-Scheck and Herchel-Bulkley model lacks behind in the purpose.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"14 ","pages":"327-341"},"PeriodicalIF":17.6,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2020.3011182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38221799","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}

引用次数: 9

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review 利用脑电信号预测癫痫发作的机器学习：综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2020-07-13 DOI: 10.1109/RBME.2020.3008792

Khansa Rasheed;Adnan Qayyum;Junaid Qadir;Shobi Sivathamboo;Patrick Kwan;Levin Kuhlmann;Terence O’Brien;Adeel Razi

{"title":"Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review","authors":"Khansa Rasheed;Adnan Qayyum;Junaid Qadir;Shobi Sivathamboo;Patrick Kwan;Levin Kuhlmann;Terence O’Brien;Adeel Razi","doi":"10.1109/RBME.2020.3008792","DOIUrl":"10.1109/RBME.2020.3008792","url":null,"abstract":"With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"14 ","pages":"139-155"},"PeriodicalIF":17.6,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/RBME.2020.3008792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38221798","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}

引用次数: 132