 Subjects -> ELECTRONICS (Total: 207 journals)
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- IEEE Engineering in Medicine and Biology Society Information
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Pages: C2 - C2
Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
PubDate:
2023
Issue No: Vol. 16 (2023)
- IEEE Reviews in Biomedical Engineering (R-BME) Information
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Pages: C3 - C3
Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Editorial A Message From the New Editor-in-Chief
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Authors:
Bin He;
Pages: 4 - 4
Abstract: Presents the introductory editorial for this issue of the publication.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Explainable Artificial Intelligence Methods in Combating Pandemics: A
Systematic Review-
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Authors:
Felipe Giuste;Wenqi Shi;Yuanda Zhu;Tarun Naren;Monica Isgut;Ying Sha;Li Tong;Mitali Gupte;May D. Wang;
Pages: 5 - 21
Abstract: Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decision-making. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses
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Authors:
Shobhit K. Patel;Jaymit Surve;Juveriya Parmar;Kawsar Ahmed;Francis M. Bui;Fahad Ahmed Al-Zahrani;
Pages: 22 - 37
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Emerging Technologies Used in Health Management and Efficiency Improvement
During Different Contact Tracing Phases Against COVID-19 Pandemic-
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Authors:
Maggie Ezzat Gaber Gendy;Mehmet Rasit Yuce;
Pages: 38 - 52
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Systematic Review of Advanced AI Methods for Improving Healthcare Data
Quality in Post COVID-19 Era-
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Authors:
Monica Isgut;Logan Gloster;Katherine Choi;Janani Venugopalan;May D. Wang;
Pages: 53 - 69
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A
Review-
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Authors:
Toufique A. Soomro;Lihong Zheng;Ahmed J. Afifi;Ahmed Ali;Shafiullah Soomro;Ming Yin;Junbin Gao;
Pages: 70 - 90
Abstract: Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Electrophysiology-Based Closed Loop Optogenetic Brain Stimulation Devices:
Recent Developments and Future Prospects-
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Authors:
Lekshmy Sudha Kumari;Abbas Z. Kouzani;
Pages: 91 - 108
Abstract: With its potential of single cell specificity, optogenetics has made the investigation into the brain circuits more controllable. Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic brain stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Graph Signal Processing, Graph Neural Network and Graph Learning on
Biological Data: A Systematic Review-
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Authors:
Rui Li;Xin Yuan;Mohsen Radfar;Peter Marendy;Wei Ni;Terrence J. O’Brien;Pablo M. Casillas-Espinosa;
Pages: 109 - 135
Abstract: Graph networks can model data observed across different levels of biological systems that span from population graphs (with patients as network nodes) to molecular graphs that involve omics data. Graph-based approaches have shed light on decoding biological processes modulated by complex interactions. This paper systematically reviews graph-based analysis methods of Graph Signal Processing (GSP), Graph Neural Networks (GNNs) and graph topology inference, and their applications to biological data. This work focuses on the algorithms of graph-based approaches and the constructions of graph-based frameworks that are adapted to a broad range of biological data. We cover the Graph Fourier Transform and the graph filter developed in GSP, which provides tools to investigate biological signals in the graph domain that can potentially benefit from the underlying graph structures. We also review the node, graph, and interaction oriented applications of GNNs with inductive and transductive learning manners for various biological targets. As a key component of graph analysis, we provide a review of graph topology inference methods that incorporate assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within this exhaustive literature collection, potentially providing insights for future research in biological sciences.
PubDate:
2023
Issue No: Vol. 16 (2023)
- A Review of Wearable Multi-Wavelength Photoplethysmography
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Authors:
Daniel Ray;Tim Collins;Sandra I. Woolley;Prasad V. S. Ponnapalli;
Pages: 136 - 151
Abstract: Optical pulse detection ‘photoplethysmography’ (PPG) provides a means of low cost and unobtrusive physiological monitoring that is popular in many wearable devices. However, the accuracy, robustness and generalizability of single-wavelength PPG sensing are sensitive to biological characteristics as well as sensor configuration and placement; this is significant given the increasing adoption of single-wavelength wrist-worn PPG devices in clinical studies and healthcare. Since different wavelengths interact with the skin to varying degrees, researchers have explored the use of multi-wavelength PPG to improve sensing accuracy, robustness and generalizability. This paper contributes a novel and comprehensive state-of-the-art review of wearable multi-wavelength PPG sensing, encompassing motion artifact reduction and estimation of physiological parameters. The paper also encompasses theoretical details about multi-wavelength PPG sensing and the effects of biological characteristics. The review findings highlight the promising developments in motion artifact reduction using multi-wavelength approaches, the effects of skin temperature on PPG sensing, the need for improved diversity in PPG sensing studies and the lack of studies that investigate the combined effects of factors. Recommendations are made for the standardization and completeness of reporting in terms of study design, sensing technology and participant characteristics.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Wearable Printed Temperature Sensors: Short Review on Latest Advances for
Biomedical Applications-
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Authors:
Saleem Khan;Shaukat Ali;Arshad Khan;Amine Bermak;
Pages: 152 - 170
Abstract: The rapid growth in wearable biosensing devices is driven by the strong desire to monitor the human health data and to predict the symptoms of chronic diseases at an early stage. Different sensors are developed for continuous monitoring of various biomarkers through wearable and implantable sensing patches. Temperature sensor has proved to be an important physiological parameter amongst the various wearable biosensing patches. This paper highlights the recent progresses made in printing of functional nanomaterials for developing wearable temperature sensors on polymeric substrates. A special focus is given to the advanced functional nanomaterials as well as their deposition through printing technologies. The geometric resolutions, shape, physical and electrical characteristics as well as sensing properties using different materials are compared and summarized. Wearability is the main concern of these newly developed sensors, which is summarized by discussing representative examples. Finally, the challenges concerning the stability, repeatability, reliability, sensitivity, linearity, ageing, and large-scale manufacturing are discussed with future outlook of the wearable systems.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging
Trends, Challenges, and Opportunities-
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Authors:
Yao Ge;Ahmad Taha;Syed Aziz Shah;Kia Dashtipour;Shuyuan Zhu;Jonathan Cooper;Qammer H. Abbasi;Muhammad Ali Imran;
Pages: 171 - 191
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Interpreting Deep Machine Learning Models: An Easy Guide for Oncologists
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Authors:
José P. Amorim;Pedro H. Abreu;Alberto Fernández;Mauricio Reyes;João Santos;Miguel H. Abreu;
Pages: 192 - 207
Abstract: Healthcare agents, in particular in the oncology field, are currently collecting vast amounts of diverse patient data. In this context, some decision-support systems, mostly based on deep learning techniques, have already been approved for clinical purposes. Despite all the efforts in introducing artificial intelligence methods in the workflow of clinicians, its lack of interpretability - understand how the methods make decisions - still inhibits their dissemination in clinical practice. The aim of this article is to present an easy guide for oncologists explaining how these methods make decisions and illustrating the strategies to explain them. Theoretical concepts were illustrated based on oncological examples and a literature review of research works was performed from PubMed between January 2014 to September 2020, using “deep learning techniques,” “interpretability” and “oncology” as keywords. Overall, more than 60% are related to breast, skin or brain cancers and the majority focused on explaining the importance of tumor characteristics (e.g. dimension, shape) in the predictions. The most used computational methods are multilayer perceptrons and convolutional neural networks. Nevertheless, despite being successfully applied in different cancers scenarios, endowing deep learning techniques with interpretability, while maintaining their performance, continues to be one of the greatest challenges of artificial intelligence.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Unsupervised ECG Analysis: A Review
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Authors:
Kasra Nezamabadi;Neda Sardaripour;Benyamin Haghi;Mohamad Forouzanfar;
Pages: 208 - 224
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation
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Authors:
Simon Bohlender;Ilkay Oksuz;Anirban Mukhopadhyay;
Pages: 225 - 240
Abstract: Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep-learning based medical image segmentation. However, the over-dependence of these methods on pixel-level classification and regression has been identified early on as a problem. Especially when trained on medical databases with sparse available annotation, these methods are prone to generate segmentation artifacts such as fragmented structures, topological inconsistencies and islands of pixel. These artifacts are especially problematic in medical imaging since segmentation is almost always a pre-processing step for some downstream evaluations like surgical planning, visualization, prognosis, or treatment planning. However, one common thread across all these downstream tasks is the demand of anatomical consistency. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models, Active Contours are becoming increasingly popular over the past 5 years. In this review paper, a broad overview of recent literature on bringing explicit anatomical constraints for medical image segmentation is given, the shortcomings and opportunities are discussed and the potential shift towards implicit shape modelling is elaborated. We review the most relevant papers published until the submission date and provide a tabulated view with method details for quick access.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Artificial Intelligence for Emerging Technology in Surgery: Systematic
Review and Validation-
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Authors:
Ephraim Nwoye;Wai Lok Woo;Bin Gao;Tobenna Anyanwu;
Pages: 241 - 259
Abstract: Surgery is a high-risk procedure of therapy and is associated to post trauma complications of longer hospital stay, estimated blood loss and long duration of surgeries. Reports have suggested that over 2.5% patients die during and post operation. This paper is aimed at systematic review of previous research on artificial intelligence (AI) in surgery, analyzing their results with suitable software to validate their research by obtaining same or contrary results. Six published research articles have been reviewed across three continents. These articles have been re-validated using software including SPSS and MedCalc to obtain the statistical features such as the mean, standard deviation, significant level, and standard error. From the significant values, the experiments are then classified according to the null (p < 0.05) or alternative (p>0.05) hypotheses. The results obtained from the analysis have suggested significant difference in operating time, docking time, staging time, and estimated blood loss but show no significant difference in length of hospital stay, recovery time and lymph nodes harvested between robotic assisted surgery using AI and normal conventional surgery. From the evaluations, this research suggests that AI-assisted surgery improves over the conventional surgery as safer and more efficient system of surgery with minimal or no complications.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Review of Eye Tracking Metrics Involved in Emotional and Cognitive
Processes-
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Authors:
Vasileios Skaramagkas;Giorgos Giannakakis;Emmanouil Ktistakis;Dimitris Manousos;Ioannis Karatzanis;Nikolaos S. Tachos;Evanthia Tripoliti;Kostas Marias;Dimitrios I. Fotiadis;Manolis Tsiknakis;
Pages: 260 - 277
Abstract: Eye behaviour provides valuable information revealing one’s higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eye-tracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Lower-Limb Medical and Rehabilitation Exoskeletons: A Review of the
Current Designs-
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Authors:
Alberto Plaza;Mar Hernandez;Gonzalo Puyuelo;Elena Garces;Elena Garcia;
Pages: 278 - 291
Abstract: Medical and rehabilitation exoskeletons are being increasingly considered by therapists when choosing a treatment for individuals affected by lower limb impairments. Although all such exoskeletons seem to provide similar features and performance, there are, in practice, significant differences among them in terms of maximum walking speed, maximum torque, weight, autonomy, interaction with the user, or even the way to use it. In this review, the state of the art of the main commercial exoskeletons is described, while analyzing their properties, advantages, and disadvantages. Three groups are considered: complete exoskeletons, partial exoskeletons and open lines of research. A comparative analysis between them is performed while considering the main scientific and technical aspects to be improved. In conclusion to this analysis, the balance between feasibility and innovation in exoskeletons development is a design challenge. Commercial exoskeletons must fulfil standards whilst ensuring their safety and robustness. However, achieving a new generation of exoskeletons means a need to implement new hardware paradigms, and to enhance control strategies focused on assist-as-needed scheme. Finally, some aspects to improve current designs of the exoskeleton are presented.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Epilepsy Detection From EEG Using Complex Network Techniques: A Review
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Authors:
Supriya Supriya;Siuly Siuly;Hua Wang;Yanchun Zhang;
Pages: 292 - 306
Abstract: Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-third of epileptic patients experience seizures attack even with medicated treatment. The menace of SUDEP (Sudden unexpected death in epilepsy) in an adult epileptic patient is approximately 8–17% more and 34% in a children epileptic patient. The expert neurologist manually analyses the Electroencephalogram (EEG) signals for epilepsy diagnosis. The non-stationary and complex nature of EEG signals this task more error-prone, time-consuming and even expensive. Hence, it is essential to develop automatic epilepsy detection techniques to ensure an appropriate identification and treatment of this disease. Nowadays, graph-theory has been considered as a prominent approach in the neuroscience field. The network-based approach characterizes a hidden sight of brain activity and brain-behavior mapping. The graph-theory not even helps to understand the underlying dynamics of EEG signals at microscopic, mesoscopic, and macroscopic level but also provide the correlation among them. This paper provides a review report about graph-theory based automated epilepsy detection methods. Furthermore, it will assist the expert's neurologist and researchers with the information of complex network-based epilepsy detection and aid the technician for developing an intelligent system that improving the diagnosis of epilepsy disorder.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Retinal OCT Image Registration: Methods and Applications
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Authors:
Lingjiao Pan;Xinjian Chen;
Pages: 307 - 318
Abstract: Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in correcting scanning artifacts, reducing speckle noise, fusing and splicing images and evaluating longitudinal disease progression are studied as well. At the end of this paper, registration of retina with serious pathology and registration with deep learning technique are also discussed.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Noise Reduction in Cochlear Implant Signal Processing: A Review and Recent
Developments-
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Authors:
Fergal Henry;Martin Glavin;Edward Jones;
Pages: 319 - 331
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. 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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Information and Communication Theoretical Understanding and Treatment of
Spinal Cord Injuries: State-of-The-Art and Research Challenges-
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Authors:
Ozgur B. Akan;Hamideh Ramezani;Meltem Civas;Oktay Cetinkaya;Bilgesu A. Bilgin;Naveed A. Abbasi;
Pages: 332 - 347
Abstract: Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio-Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research.
PubDate:
2023
Issue No: Vol. 16 (2023)
- A Systematic Review on Methods and Tools for the In Situ Fenestration of
Aortic Stent-Graft-
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Authors:
Roberta Piazza;M. Carbone;R. N. Berchiolli;V. Ferrari;M. Ferrari;S. Condino;
Pages: 348 - 356
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- What Ultrasound Can and Cannot Do in Implantable Medical Device
Communications-
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Authors:
Banafsaj Jaafar;Jeff Neasham;Patrick Degenaar;
Pages: 357 - 370
Abstract: Modern Active Medical Implantable Devices require communications to transmit information to the outside world or other implantable sub-systems. This can include physiological data, diagnostics, and parameters to optimise the therapeutic protocol. The available options are to use optical, radiofrequency, or ultrasonic communications. However, in all cases, transmission becomes more difficult with deeper transmission through tissue. Challenges include absorption and scattering by tissue, and the need to ensure there are no undesirable heating effects. As such, this paper aims to review research progress in using ultrasound as an alternative for deep tissue communications. We provide an empirical review of the technology and communication protocols that different groups have used, as well as comparing the implications in terms of penetration depth, implant size, and data rate. We conclude that this technique has promise for deeper implants and for intrabody communications between implantable devices (intrabody networks).
PubDate:
2023
Issue No: Vol. 16 (2023)
- A Review of Neuroimaging-Driven Brain Age Estimation for Identification of
Brain Disorders and Health Conditions-
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Authors:
Shiwangi Mishra;Iman Beheshti;Pritee Khanna;
Pages: 371 - 385
Abstract: 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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Exploring the Potential of Stem Cell-Based Therapy for Aesthetic and
Plastic Surgery-
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Authors:
Dang-Khoa Tran;Thuy Nguyen Thi Phuong;Nhat-Le Bui;Vijai Singh;Qi Hao Looi;Benson Koh;Ungku Mohd Shahrin B Mohd Zaman;Jhi Biau Foo;Chia-Ching Wu;Pau Loke Show;Dinh-Toi Chu;
Pages: 386 - 402
Abstract: Over the last decade, stem cell-associated therapies are widely used because of their potential in self-renewable and multipotent differentiation ability. Stem cells have become more attractive for aesthetic uses and plastic surgery, including scar reduction, breast augmentation, facial contouring, hand rejuvenation, and anti-aging. The current preclinical and clinical studies of stem cells on aesthetic uses also showed promising outcomes. Adipose-derived stem cells are commonly used for fat grafting that demonstrated scar improvement, anti-aging, skin rejuvenation properties, etc. While stem cell-based products have yet to receive approval from the FDA for aesthetic medicine and plastic surgery. Moving forward, the review on the efficacy and potential of stem cell-based therapy for aesthetic and plastic surgery is limited. In the present review, we discuss the current status and recent advances of using stem cells for aesthetic and plastic surgery. The potential of cell-free therapy and tissue engineering in this field is also highlighted. The clinical applications, advantages, and limitations are also discussed. This review also provides further works that need to be investigated to widely apply stem cells in the clinic, especially in aesthetic and plastic contexts.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Hemodynamic Modeling, Medical Imaging, and Machine Learning and Their
Applications to Cardiovascular Interventions-
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Authors:
Mason Kadem;Louis Garber;Mohamed Abdelkhalek;Baraa K. Al-Khazraji;Zahra Keshavarz-Motamed;
Pages: 403 - 423
Abstract: Cardiovascular disease is a deadly global health crisis that carries a substantial financial burden. Innovative treatment and management of cardiovascular disease straddles medicine, personalized hemodynamic modeling, machine learning, and modern imaging to help improve patient outcomes and reduce the economic impact. Hemodynamic modeling offers a non-invasive method to provide clinicians with new pre- and post- procedural metrics and aid in the selection of treatment options. Medical imaging is an integral part in clinical workflows for understanding and managing cardiac disease and interventions. Coupling machine learning with modeling, and cardiovascular imaging, provides faster modeling, improved data fidelity, and an enhanced understanding and earlier detection of cardiovascular anomalies, leading to the development of patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. Herein, we provide a scoping review of translational hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. We particularly focus on providing an intuitive understanding of each of these approaches and their ability to support decision making during important clinical milestones.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Advances in Non-Invasive Blood Pressure Measurement Techniques
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Authors:
Tuukka Panula;Jukka-Pekka Sirkiä;David Wong;Matti Kaisti;
Pages: 424 - 438
Abstract: Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-of-concept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Follow-The-Leader Mechanisms in Medical Devices: A Review on Scientific
and Patent Literature-
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Authors:
Costanza Culmone;Fatih S. Yikilmaz;Fabian Trauzettel;Paul Breedveld;
Pages: 439 - 455
Abstract: Conventional medical instruments are not capable of passing through tortuous anatomy as required for natural orifice transluminal endoscopic surgery due to their rigid shaft designs. Nevertheless, developments in minimally invasive surgery are pushing medical devices to become more dexterous. Amongst devices with controllable flexibility, so-called Follow-The-Leader (FTL) devices possess motion capabilities to pass through confined spaces without interacting with anatomical structures. The goal of this literature study is to provide a comprehensive overview of medical devices with FTL motion. A scientific and patent literature search was performed in five databases (Scopus, PubMed, Web of Science, IEEExplore, Espacenet). Keywords were used to isolate FTL behavior in devices with medical applications. Ultimately, 35 unique devices were reviewed and categorized. Devices were allocated according to their design strategies to obtain the three fundamental sub-functions of FTL motion: steering, (controlling the leader/end-effector orientation), propagation, (advancing the device along a specific path), and conservation (memorizing the shape of the path taken by the device). A comparative analysis of the devices was carried out, showing the commonly used design choices for each sub-function and the different combinations. The advantages and disadvantages of the design aspects and an overview of their performance were provided. Devices that were initially assessed as ineligible were considered in a possible medical context or presented with FTL potential, broadening the classification. This review could aid in the development of a new generation of FTL devices by providing a comprehensive overview of the current solutions and stimulating the search for new ones.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Phenocopying Glioblastoma: A Review
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Authors:
Mariam-Eleni Oraiopoulou;Eleftheria Tzamali;Joseph Papamatheakis;Vangelis Sakkalis;
Pages: 456 - 471
Abstract: The main reason why therapeutic schemes fail in Glioblastoma lies on its own peculiarities as a cancer and on our failure to fully decipher them. Fast tumor evolution, invasiveness and incomplete surgical resection contribute to disease recurrence, therapy resistance and high mortality. More faithful models must be developed to address Glioblastoma biology and better clinical guidance. Research studies are discussed in this review that: i) improve understanding and assessment of the growth mechanisms of Glioblastoma and ii) develop preclinical models (in vitro-in vivo-in silico) that mimic patient's tumor (phenocopying) in order to provide better prediction of response to therapies.
PubDate:
2023
Issue No: Vol. 16 (2023)
- A Review of Techniques for Surface Electromyography Signal Quality
Analysis-
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Authors:
Emma Farago;Dawn MacIsaac;Michelle Suk;Adrian D. C. Chan;
Pages: 472 - 486
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Virtual Reality Assisted Motor Imagery for Early Post-Stroke Recovery: A
Review-
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Authors:
Chi Sang Choy;Shaun L. Cloherty;Elena Pirogova;Qiang Fang;
Pages: 487 - 498
Abstract: 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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- The Application of Nanotechnology for Quantification of Circulating Tumour
DNA in Liquid Biopsies: A Systematic Review-
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Authors:
Nathan J. W. Wu;Matthew Aquilina;Bin-Zhi Qian;Remco Loos;Ines Gonzalez-Garcia;Cristina C. Santini;Katherine E. Dunn;
Pages: 499 - 513
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Robotic Simulators for Tissue Examination Training With Multimodal Sensory
Feedback-
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Authors:
Liang He;Perla Maiolino;Florence Leong;Thilina Dulantha Lalitharatne;Simon de Lusignan;Mazdak Ghajari;Fumiya Iida;Thrishantha Nanayakkara;
Pages: 514 - 529
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain
Injury-
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Authors:
Georgia Harris;Jonathan James Stanley Rickard;Gibran Butt;Liam Kelleher;Richard James Blanch;Jonathan Cooper;Pola Goldberg Oppenheimer;
Pages: 530 - 559
Abstract: 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
PubDate:
2023
Issue No: Vol. 16 (2023)
- Neuromuscular Controller Models for Quantifying Standing Balance in Older
People: A Systematic Review-
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Authors:
Fredrik Olsson;Kjartan Halvorsen;Anna Cristina Åberg;
Pages: 560 - 578
Abstract: Objective quantification of the balancing mechanisms in humans is strongly needed in health care of older people, yet is largely missing among current clinical balance assessment methods. Hence, the main goal of this literature review is to identify methods that have the potential to meet that need. We searched in the PubMed and IEEE Xplore databases using predefined criteria, screened 1064 articles, and systematically reviewed and categorized methods from 73 studies that deal with identification of neuromuscular controller models of human upright standing from empirical data. These studies were then analyzed with the particular aim to understand to what degree such methods would be useful solutions for assessing the balance of older individuals aged above 60 years. The 16 studies that included an older subject population were especially examined with this in mind. The majority of the reviewed articles focused on research questions related to the general function of human balance control rather than clinical applicability. Further efforts need to be made to adapt these methods for more accessible and mobile technologies and to ensure that the outcomes are valid for balance assessment of a general older population.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Deep Learning With Radiogenomics Towards Personalized Management of
Gliomas-
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Authors:
Sushmita Mitra;
Pages: 579 - 593
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Bioprinting: A Strategy to Build Informative Models of Exposure and
Disease-
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Authors:
Jose Caceres-Alban;Midori Sanchez;Fanny L. Casado;
Pages: 594 - 610
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 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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Data Transformation in the Processing of Neuronal Signals: A Powerful Tool
to Illuminate Informative Contents-
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Authors:
MohammadAli Shaeri;Amir M. Sodagar;
Pages: 611 - 626
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- CMOS Time-to-Digital Converters for Biomedical Imaging Applications
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Authors:
Ryan Scott;Wei Jiang;M. Jamal Deen;
Pages: 627 - 652
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.
PubDate:
2023
Issue No: Vol. 16 (2023)
- A Review of Recent Advances and Future Developments in Fetal
Phonocardiography-
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Authors:
Radana Kahankova;Martina Mikolasova;Rene Jaros;Katerina Barnova;Martina Ladrova;Radek Martinek;
Pages: 653 - 671
Abstract: Fetal phonocardiography (fPCG) is receiving attention as it is a promising method for continuous fetal monitoring due to its non-invasive and passive nature. However, it suffers from the interference from various sources, overlapping the desired signal in the time and frequency domains. This paper introduces the state-of-the-art methods used for fPCG signal extraction and processing, as well as means of detection and classification of various features defining fetal health state. It also provides an extensive summary of remaining challenges, along with the practical insights and suggestions for the future research directions.
PubDate:
2023
Issue No: Vol. 16 (2023)
- A Mapping Review of Real-Time Movement Sonification Systems for Movement
Rehabilitation-
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Authors:
Thomas H. Nown;Priti Upadhyay;Andrew Kerr;Ivan Andonovic;Christos Tachtatzis;Madeleine A. Grealy;
Pages: 672 - 686
Abstract: Movement sonification is emerging as a useful tool for rehabilitation, with increasing evidence in support of its use. To create such a system requires component considerations outside of typical sonification design choices, such as the dimension of movement to sonify, section of anatomy to track, and methodology of motion capture. This review takes this emerging and highly diverse area of literature and keyword-code existing real-time movement sonification systems, to analyze and highlight current trends in these design choices, as such providing an overview of existing systems. A combination of snowballing through relevant existing reviews and a systematic search of multiple databases were utilized to obtain a list of projects for data extraction. The review categorizes systems into three sections: identifying the link between physical dimension to auditory dimension used in sonification, identifying the target anatomy tracked, identifying the movement tracking system used to monitor the target anatomy. The review proceeds to analyze the systematic mapping of the literature and provide results of the data analysis highlighting common and innovative design choices used, irrespective of application, before discussing the findings in the context of movement rehabilitation. A database containing the mapped keywords assigned to each project are submitted with this review.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Engineering Approaches for Breast Cancer Diagnosis: A Review
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Authors:
Arif Mohd. Kamal;Tushar Sakorikar;Uttam M. Pal;Hardik J. Pandya;
Pages: 687 - 705
Abstract: Breast cancer is a leading cause of mortality among women. The patient's survival rate is uncertain due to the limitations in the accuracy of diagnosis and effective monitoring during cancer treatment. The key to efficaciously controlling cancer on a larger scale is effective diagnosis at an early stage of cancer by distinguishing the vital signatures of the diseased from the normal breast tissue. The breast tissue is a heterogeneous turbid media that exhibits multifaceted bulk tissue properties. Various sensing modalities can yield distinct tissue behavior for cancer and adjacent normal tissues, serving as a basis for cancer diagnosis. A novel multimodal diagnostic tool that can concurrently assess the optical, electrical, and mechanical bulk tissue properties can substantially augment the clinical findings such as histopathology, potentially aiding the clinician to establish an accurate and rapid diagnosis of cancer. This review aims to discuss the clinical and engineering aspects along with the unmet challenges of these physical sensing modalities, primarily in the field of optical, electrical, and mechanical. The challenges of combining two or more of these sensing modalities that can significantly enhance the effectiveness of the clinical diagnostic tools are further investigated.
PubDate:
2023
Issue No: Vol. 16 (2023)
- Dynamical Models in Neuroscience From a Closed-Loop Control Perspective
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Authors:
Sebastián Martínez;Demián García-Violini;Mariano Belluscio;Joaquín Piriz;Ricardo Sánchez-Peña;
Pages: 706 - 721
Abstract: Modifying neural activity is a substantial goal in neuroscience that facilitates the understanding of brain functions and the development of medical therapies. Neurobiological models play an essential role, contributing to the understanding of the underlying brain dynamics. In this context, control systems represent a fundamental tool to provide a correct articulation between model stimulus (system inputs) and outcomes (system outputs). However, throughout the literature there is a lack of discussions on neurobiological models, from the formal control perspective. In general, existing control proposals applied to this family of systems, are developed empirically, without theoretical and rigorous framework. Thus, the existing control solutions, present clear and significant limitations. The focus of this work is to survey dynamical neurobiological models that could serve for closed-loop control schemes or for simulation analysis. Consequently, this paper provides a comprehensive guide to discuss and analyze control-oriented neurobiological models. It also provides a potential framework to adequately tackle control problems that could modify the behavior of single neurons or networks. Thus, this study constitutes a key element in the upcoming discussions and studies regarding control methodologies applied to neurobiological systems, to extend the present research and understanding horizon for this field.
PubDate:
2023
Issue No: Vol. 16 (2023)
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Hybrid journal (It can contain Open Access articles)
