Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators.
{"title":"Robotic Simulators for Tissue Examination Training With Multimodal Sensory Feedback","authors":"Liang He;Perla Maiolino;Florence Leong;Thilina Dulantha Lalitharatne;Simon de Lusignan;Mazdak Ghajari;Fumiya Iida;Thrishantha Nanayakkara","doi":"10.1109/RBME.2022.3168422","DOIUrl":"10.1109/RBME.2022.3168422","url":null,"abstract":"Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"514-529"},"PeriodicalIF":17.6,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9720072","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 : 2022-04-05DOI: 10.1109/RBME.2022.3164797
Emma Farago;Dawn MacIsaac;Michelle Suk;Adrian D. C. Chan
Electromyography (EMG) signals are instrumental in a variety of applications including prosthetic control, muscle health assessment, rehabilitation, and workplace monitoring. Signal contaminants including noise, interference, and artifacts can degrade the quality of the EMG signal, leading to misinterpretation; therefore it is important to ensure that collected EMG signals are of sufficient quality prior to further analysis. A literature search was conducted to identify current approaches for detecting, identifying, and quantifying contaminants within surface EMG signals. We identified two main strategies: 1) bottom-up approaches for identifying specific and well-characterized contaminants and 2) top-down approaches for detecting anomalous EMG signals or outlier channels in high-density EMG arrays. The best type(s) of approach are dependent on the circumstances of data collection including the environment, the susceptibility of the application to contaminants, and the resilience of the application to contaminants. Further research is needed for assessing EMG with multiple simultaneous contaminants, identifying ground-truths for clean EMG data, and developing user-friendly and autonomous methods for EMG signal quality analysis.
{"title":"A Review of Techniques for Surface Electromyography Signal Quality Analysis","authors":"Emma Farago;Dawn MacIsaac;Michelle Suk;Adrian D. C. Chan","doi":"10.1109/RBME.2022.3164797","DOIUrl":"10.1109/RBME.2022.3164797","url":null,"abstract":"Electromyography (EMG) signals are instrumental in a variety of applications including prosthetic control, muscle health assessment, rehabilitation, and workplace monitoring. Signal contaminants including noise, interference, and artifacts can degrade the quality of the EMG signal, leading to misinterpretation; therefore it is important to ensure that collected EMG signals are of sufficient quality prior to further analysis. A literature search was conducted to identify current approaches for detecting, identifying, and quantifying contaminants within surface EMG signals. We identified two main strategies: 1) bottom-up approaches for identifying specific and well-characterized contaminants and 2) top-down approaches for detecting anomalous EMG signals or outlier channels in high-density EMG arrays. The best type(s) of approach are dependent on the circumstances of data collection including the environment, the susceptibility of the application to contaminants, and the resilience of the application to contaminants. Further research is needed for assessing EMG with multiple simultaneous contaminants, identifying ground-truths for clean EMG data, and developing user-friendly and autonomous methods for EMG signal quality analysis.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"472-486"},"PeriodicalIF":17.6,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9365457","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 : 2022-04-05DOI: 10.1109/RBME.2022.3165062
Chi Sang Choy;Shaun L. Cloherty;Elena Pirogova;Qiang Fang
Stroke is a serious neurological disease that may lead to long-term disabilities and even death for stroke patients worldwide. The acute period, ( $le$ 1 mo post-stroke), is crucial for rehabilitation but the current standard clinical practice may be ineffective for patients with severe motor impairment, since most rehabilitation programs involve physical movement. Imagined movement – the so-called motor imagery (MI) – has been shown to activate motor areas of the brain without physical movement. MI therefore offers an opportunity for early rehabilitation of stroke patients. MI, however, is not widely employed in clinical practice due to a lack of evidence-based research. Here, we review MI-based approaches to rehabilitation of stroke patients and immersive virtual reality (VR) technologies to potentially assist MI and thus, promote recovery of motor function.
{"title":"Virtual Reality Assisted Motor Imagery for Early Post-Stroke Recovery: A Review","authors":"Chi Sang Choy;Shaun L. Cloherty;Elena Pirogova;Qiang Fang","doi":"10.1109/RBME.2022.3165062","DOIUrl":"10.1109/RBME.2022.3165062","url":null,"abstract":"Stroke is a serious neurological disease that may lead to long-term disabilities and even death for stroke patients worldwide. The acute period, (\u0000<inline-formula><tex-math>$le$</tex-math></inline-formula>\u00001 mo post-stroke), is crucial for rehabilitation but the current standard clinical practice may be ineffective for patients with severe motor impairment, since most rehabilitation programs involve physical movement. Imagined movement – the so-called motor imagery (MI) – has been shown to activate motor areas of the brain without physical movement. MI therefore offers an opportunity for early rehabilitation of stroke patients. MI, however, is not widely employed in clinical practice due to a lack of evidence-based research. Here, we review MI-based approaches to rehabilitation of stroke patients and immersive virtual reality (VR) technologies to potentially assist MI and thus, promote recovery of motor function.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"487-498"},"PeriodicalIF":17.6,"publicationDate":"2022-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09749920.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9735257","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 : 2022-03-23DOI: 10.1109/RBME.2022.3161352
Georgia Harris;Jonathan James Stanley Rickard;Gibran Butt;Liam Kelleher;Richard James Blanch;Jonathan Cooper;Pola Goldberg Oppenheimer
The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the “silent epidemic” is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI
{"title":"Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury","authors":"Georgia Harris;Jonathan James Stanley Rickard;Gibran Butt;Liam Kelleher;Richard James Blanch;Jonathan Cooper;Pola Goldberg Oppenheimer","doi":"10.1109/RBME.2022.3161352","DOIUrl":"10.1109/RBME.2022.3161352","url":null,"abstract":"The study of ocular manifestations of neurodegenerative disorders, \u0000<italic>Oculomics,</i>\u0000 is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the “silent epidemic” is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"530-559"},"PeriodicalIF":17.6,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9740402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9365613","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 : 2022-03-18DOI: 10.1109/RBME.2022.3159389
Nathan J. W. Wu;Matthew Aquilina;Bin-Zhi Qian;Remco Loos;Ines Gonzalez-Garcia;Cristina C. Santini;Katherine E. Dunn
Technologies for quantifying circulating tumour DNA (ctDNA) in liquid biopsies could enable real-time measurements of cancer progression, profoundly impacting patient care. Sequencing methods can be too complex and time-consuming for regular point-of-care monitoring, but nanotechnology offers an alternative, harnessing the unique properties of objects tens to hundreds of nanometres in size. This systematic review was performed to identify all examples of nanotechnology-based ctDNA detection and assess their potential for clinical use. Google Scholar, PubMed, Web of Science, Google Patents, Espacenet and Embase/MEDLINE were searched up to 23rd March 2021. The review identified nanotechnology-based methods for ctDNA detection for which quantitative measures (e.g., limit of detection, LOD) were reported and biologically relevant samples were used. The pre-defined inclusion criteria were met by 66 records. LODs ranged from 10 zM to 50nM. 25 records presented an LOD of 10fM or below. Nanotechnology-based approaches could provide the basis for the next wave of advances in ctDNA diagnostics, enabling analysis at the point-of-care, but none are currently used clinically. Further work is needed in development and validation; trade-offs are expected between different performance measures e.g., number of sequences detected and time to result.
{"title":"The Application of Nanotechnology for Quantification of Circulating Tumour DNA in Liquid Biopsies: A Systematic Review","authors":"Nathan J. W. Wu;Matthew Aquilina;Bin-Zhi Qian;Remco Loos;Ines Gonzalez-Garcia;Cristina C. Santini;Katherine E. Dunn","doi":"10.1109/RBME.2022.3159389","DOIUrl":"10.1109/RBME.2022.3159389","url":null,"abstract":"Technologies for quantifying circulating tumour DNA (ctDNA) in liquid biopsies could enable real-time measurements of cancer progression, profoundly impacting patient care. Sequencing methods can be too complex and time-consuming for regular point-of-care monitoring, but nanotechnology offers an alternative, harnessing the unique properties of objects tens to hundreds of nanometres in size. This systematic review was performed to identify all examples of nanotechnology-based ctDNA detection and assess their potential for clinical use. Google Scholar, PubMed, Web of Science, Google Patents, Espacenet and Embase/MEDLINE were searched up to 23rd March 2021. The review identified nanotechnology-based methods for ctDNA detection for which quantitative measures (e.g., limit of detection, LOD) were reported and biologically relevant samples were used. The pre-defined inclusion criteria were met by 66 records. LODs ranged from 10 zM to 50nM. 25 records presented an LOD of 10fM or below. Nanotechnology-based approaches could provide the basis for the next wave of advances in ctDNA diagnostics, enabling analysis at the point-of-care, but none are currently used clinically. Further work is needed in development and validation; trade-offs are expected between different performance measures e.g., number of sequences detected and time to result.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"499-513"},"PeriodicalIF":17.6,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09737698.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9371134","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 : 2022-03-07DOI: 10.1109/RBME.2022.3156810
Yao Ge;Ahmad Taha;Syed Aziz Shah;Kia Dashtipour;Shuyuan Zhu;Jonathan Cooper;Qammer H. Abbasi;Muhammad Ali Imran
WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users’ bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.
{"title":"Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging Trends, Challenges, and Opportunities","authors":"Yao Ge;Ahmad Taha;Syed Aziz Shah;Kia Dashtipour;Shuyuan Zhu;Jonathan Cooper;Qammer H. Abbasi;Muhammad Ali Imran","doi":"10.1109/RBME.2022.3156810","DOIUrl":"10.1109/RBME.2022.3156810","url":null,"abstract":"WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users’ bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"171-191"},"PeriodicalIF":17.6,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09729463.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9713747","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}
Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the large amount of data produced daily by cardiac monitors. As thenumber of abnormal ECG samples with cardiologist-supplied labels required to train supervised machine learning models is limited, there is a growing need for unsupervised learning methods for ECG analysis. Unsupervised learning aims to partition ECG samples into distinct abnormality classes without cardiologist-supplied labels–a process referred to as ECG clustering. In addition to abnormality detection, ECG clustering has recently discovered inter and intra-individual patterns that reveal valuable information about the whole body and mind, such as emotions, mental disorders, and metabolic levels. ECG clustering can also resolve specific challenges facing supervised learning systems, such as the imbalanced data problem, and can enhance biometric systems. While several reviews exist on supervised ECG systems, a comprehensive review of unsupervised ECG analysis techniques is still lacking. This study reviews ECG clustering techniques developed mainly in the last decade. The focus will be on recent machine learning and deep learning algorithms and their practical applications. We critically review and compare these techniques, discuss their applications and limitations, and provide future research directions. This review provides further insights into ECG clustering and presents the necessary information required to adopt the appropriate algorithm for a specific application.
{"title":"Unsupervised ECG Analysis: A Review","authors":"Kasra Nezamabadi;Neda Sardaripour;Benyamin Haghi;Mohamad Forouzanfar","doi":"10.1109/RBME.2022.3154893","DOIUrl":"10.1109/RBME.2022.3154893","url":null,"abstract":"Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the large amount of data produced daily by cardiac monitors. As thenumber of abnormal ECG samples with cardiologist-supplied labels required to train supervised machine learning models is limited, there is a growing need for unsupervised learning methods for ECG analysis. Unsupervised learning aims to partition ECG samples into distinct abnormality classes without cardiologist-supplied labels–a process referred to as ECG clustering. In addition to abnormality detection, ECG clustering has recently discovered inter and intra-individual patterns that reveal valuable information about the whole body and mind, such as emotions, mental disorders, and metabolic levels. ECG clustering can also resolve specific challenges facing supervised learning systems, such as the imbalanced data problem, and can enhance biometric systems. While several reviews exist on supervised ECG systems, a comprehensive review of unsupervised ECG analysis techniques is still lacking. This study reviews ECG clustering techniques developed mainly in the last decade. The focus will be on recent machine learning and deep learning algorithms and their practical applications. We critically review and compare these techniques, discuss their applications and limitations, and provide future research directions. This review provides further insights into ECG clustering and presents the necessary information required to adopt the appropriate algorithm for a specific application.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"208-224"},"PeriodicalIF":17.6,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9728418","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 : 2022-02-14DOI: 10.1109/RBME.2022.3151340
MohammadAli Shaeri;Amir M. Sodagar
Neuroscientists seek efficient solutions for deciphering the sophisticated unknowns of the brain. Effective development of complicated brain-related tools is the focal point of research in neuroscience and neurotechnology. Thanks to today’s technological advancements, the physical development of high-density and high-resolution neural interfaces has been made possible. This is where the critical bottleneck in receiving the expected functionality from such devices shifts to transferring, processing, and subsequently analyzing the massive neurophysiological extra-cellular data recorded. To respond to this inevitable concern, a spectrum of neuronal signal processing techniques have been proposed to extract task-related informative content of the signals conveying neuronal activities, and eliminate the irrelevant contents. Such techniques provide powerful tools for a wide range of neuroscience research, from low-level perception to high-level cognition. Data transformations are among the most efficient processing techniques that serve this purpose by properly changing the data representation. Mapping the data from its original domain (i.e., the time-space domain) to a new representational domain, data transformations change the viewing angle of observing the informative content of the data. This paper reviews the employment of data transformations in order to process neuronal signals and their three key applications, including spike detection, spike sorting, and data compression.
{"title":"Data Transformation in the Processing of Neuronal Signals: A Powerful Tool to Illuminate Informative Contents","authors":"MohammadAli Shaeri;Amir M. Sodagar","doi":"10.1109/RBME.2022.3151340","DOIUrl":"10.1109/RBME.2022.3151340","url":null,"abstract":"Neuroscientists seek efficient solutions for deciphering the sophisticated unknowns of the brain. Effective development of complicated brain-related tools is the focal point of research in neuroscience and neurotechnology. Thanks to today’s technological advancements, the physical development of high-density and high-resolution neural interfaces has been made possible. This is where the critical bottleneck in receiving the expected functionality from such devices shifts to transferring, processing, and subsequently analyzing the massive neurophysiological extra-cellular data recorded. To respond to this inevitable concern, a spectrum of neuronal signal processing techniques have been proposed to extract task-related informative content of the signals conveying neuronal activities, and eliminate the irrelevant contents. Such techniques provide powerful tools for a wide range of neuroscience research, from low-level perception to high-level cognition. Data transformations are among the most efficient processing techniques that serve this purpose by properly changing the data representation. Mapping the data from its original domain (i.e., the time-space domain) to a new representational domain, data transformations change the viewing angle of observing the informative content of the data. This paper reviews the employment of data transformations in order to process neuronal signals and their three key applications, including spike detection, spike sorting, and data compression.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"611-626"},"PeriodicalIF":17.6,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9358694","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 : 2022-01-27DOI: 10.1109/RBME.2022.3146293
Jose Caceres-Alban;Midori Sanchez;Fanny L. Casado
Novel additive manufacturing techniques are revolutionizing fields of industry providing more dimensions to control and the versatility of fabricating multi-material products. Medical applications hold great promise to manufacture constructs of mixed biologically compatible materials together with functional cells and tissues. We reviewed technologies and promising developments nurturing innovation of physiologically relevant models to study safety of chemicals that are hard to reproduce in current models, or diseases for which there are no models available. Extrusion-, inkjet- and laser-assisted bioprinting are the most used techniques. Hydrogels as constituents of bioinks and biomaterial inks are the most versatile materials to recreate physiological and pathophysiological microenvironments. The highlighted bioprinted models were chosen because they guarantee post-printing cellular viability while maintaining desirable mechanical properties of their constitutive bioinks or biomaterial inks to ensure their printability. Bioprinting is being readily adopted to overcome ethical concerns of in vivo models and improve the automation, reproducibility, geometry stability of traditional in vitro models. The challenges for advancing the technological level readiness of bioprinting require overcoming heterogeneity, microstructural complexity, dynamism and integration with other models, to generate multi-organ platforms that can inform about biological responses to chemical exposure, disease development and efficacy of novel therapies.
{"title":"Bioprinting: A Strategy to Build Informative Models of Exposure and Disease","authors":"Jose Caceres-Alban;Midori Sanchez;Fanny L. Casado","doi":"10.1109/RBME.2022.3146293","DOIUrl":"10.1109/RBME.2022.3146293","url":null,"abstract":"Novel additive manufacturing techniques are revolutionizing fields of industry providing more dimensions to control and the versatility of fabricating multi-material products. Medical applications hold great promise to manufacture constructs of mixed biologically compatible materials together with functional cells and tissues. We reviewed technologies and promising developments nurturing innovation of physiologically relevant models to study safety of chemicals that are hard to reproduce in current models, or diseases for which there are no models available. Extrusion-, inkjet- and laser-assisted bioprinting are the most used techniques. Hydrogels as constituents of bioinks and biomaterial inks are the most versatile materials to recreate physiological and pathophysiological microenvironments. The highlighted bioprinted models were chosen because they guarantee post-printing cellular viability while maintaining desirable mechanical properties of their constitutive bioinks or biomaterial inks to ensure their printability. Bioprinting is being readily adopted to overcome ethical concerns of in vivo models and improve the automation, reproducibility, geometry stability of traditional \u0000<italic>in vitro</i>\u0000 models. The challenges for advancing the technological level readiness of bioprinting require overcoming heterogeneity, microstructural complexity, dynamism and integration with other models, to generate multi-organ platforms that can inform about biological responses to chemical exposure, disease development and efficacy of novel therapies.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"594-610"},"PeriodicalIF":17.6,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09695320.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9371124","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 : 2022-01-20DOI: 10.1109/RBME.2021.3130485
Yuan-Ting Zhang
Presents the editorial for this issue of the publication.
介绍本期出版物的社论。
{"title":"Editorial: A Message From the Outgoing Editor-in-Chief","authors":"Yuan-Ting Zhang","doi":"10.1109/RBME.2021.3130485","DOIUrl":"https://doi.org/10.1109/RBME.2021.3130485","url":null,"abstract":"Presents the editorial for this issue of the publication.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"3-3"},"PeriodicalIF":17.6,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/9686800/09686995.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67748171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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