Relationship between the Res1, resonant frequency and bandwidth.
\r\n\tBasic science studies have provided new insights into the pathophysiology of β-thalassemia. Studies of genotypic and phenotypic heterogeneity among patients and a better understanding of the control of erythropoiesis have provided new targets for designing novel agents that can be tailored to individual patient needs. JAK-2 kinase inhibitors and agents targeting the GDF-11/SMAD pathway are in clinical trials.
\r\n\r\n\tThis book will attempt to discuss the historical background of the disease and present the most up-to-date material regarding disease management in today's world for the reader to be updated on the best practice management of the disease.
",isbn:"978-1-83969-158-4",printIsbn:"978-1-83969-157-7",pdfIsbn:"978-1-83969-159-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"23abb2fecebc48a2df8a954eb8378930",bookSignature:"Dr. Akshat Jain",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10727.jpg",keywords:"History of Gene Mutation, Genetic Counselling, Anemia, Genotyping, Hemoglobin Electrophoresis, HLA typing, Hemolysis, Aplastic Anemia, Blood Transfusion, Laboratory Testing, Fetal Hemoglobin Modifiers, Gene Therapy",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 4th 2021",dateEndSecondStepPublish:"March 4th 2021",dateEndThirdStepPublish:"May 3rd 2021",dateEndFourthStepPublish:"July 22nd 2021",dateEndFifthStepPublish:"September 20th 2021",remainingDaysToSecondStep:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"A board-certified pediatrician with a specialization in pediatric hematology-oncology and stem cell transplantation. In collaboration with Harvard Medical School, he studied and reported the outcomes of a global hemophilia collaboration. He is a member of the American Board of Pediatrics, Hematology, and American Board of Pediatrics, also he is a Committee member for the American Society of Pediatric Hematology-Oncology Special Interest Group in Global Pediatric Hematology oncology.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"344600",title:"Prof.",name:"Akshat",middleName:null,surname:"Jain",slug:"akshat-jain",fullName:"Akshat Jain",profilePictureURL:"https://mts.intechopen.com/storage/users/344600/images/system/344600.jpg",biography:"Akshat Jain M.D. M.P.H.\n11175 Campus Street \nLoma Linda, California 92354\nPhone: (917) 331-3216\nakshatjainusa@gmail.com \n\nMEDICAL EDUCATION \n●\tS.S.R. Medical College, Belle Rive, Mauritius - MBBS, Bachelor of Medicine Bachelor of Surgery, 2007\n●\tPediatrics Residency Training ,The New York Medical College, Metropolitan Hospital , Dec2008-Dec 2011\n●\tPediatric Hematology Oncology and Stem Cell Transplant Fellowship, Cohen’s Children's Hospital of New York at LIJ-North Shore Health system. July 2012- September 2015\n●\tMaster’s in Public Health ,Hofstra University School of Public Health ,New York , August 2015\n\n\nHONORS/ AWARDS \n●\tThe New York Academy of Medicine Honorary Associate Award , December 2009\n●\tProgram Leadership Award - Committee of Interns and Residents (C.I.R./SIEU), April 2010\n●\tAmerican Academy of Pediatrics Program Delegate Award, New York Medical College, December 2010.\n●\tCitation of Honor from New York County for Excellence in Medicine and Service to Long Island, New York,Nassau county executive chambers , August 15,2015 \n●\tTimes of India N.R.I. ( Non Resident Achiever ) award , August 2015 \n●\tCertificate for academic excellence –Hofstra University School of Health Science & Human Services, New York August 26, 2015\n●\tAmerican Society of Hematology Leadership Institute Award , April 2016\n●\tGlobal Health Speaker Award , convener of Global Health Symposium, Hofstra NorthWell School of Medicine and School of Public health , May 2016\n●\tInternational Pediatric Lymphoma Meeting ,Session Chairperson of Pediatric Lymphoma , Indian Society of Hematology and Oncology , November 2016\n●\tContent Leader Award for Hematology perspective’s in the Global CoronaVirus Pandemic Preparedness Response for Medical Association of physicians of Indian Origin, April 2020.\n●\tConvener and Chairperson International Webinar for COVID 19 Coagulopathy, May 2020. \n●\tFeatured in the Top Doctors magazine 2020, ranked top pediatric Hematologist Oncologist for Southern California.\n\nNATIONAL/INTERNATIONAL POSITIONS \n●\tHofstra University Dean Advisory Board for the School of Health Professions, December 2017\n●\tEditorial Board – American Society of Pediatric Hematology Oncology Communications Committee, International Journal of Hematology Research (ISSN 2409-3548)\n●\tReviewer - JAMA Pediatrics (ISSN: 2168-6203), British Medical Journal (ISSN, 1468-5833), JAMA Oncology (ISSN: 2374-2437), International Journal of Hematology Research (ISSN 2394—806X), Journal of Pediatric Hematology and Oncology (ISSN: 1536-3678), New England Journal of Medicine (Resident 360). \n●\tMember – Core committee: American Cancer Society (A.C.S.) and American Academy of Pediatrics (A.A.P.) - Joint global pediatric Oncology taskforce.\n●\tAdvisor -World Health Organization, South East Asia for maternal and child health initiatives.( 2013-Ongoing) , Ministry of Health and Family Welfare ,Government of India ( 2014- Ongoing ) , American Academy of Pediatrics &American Cancer Society Global Taskforce on Pediatric Cancers.( 2014-Ongoing )\n●\tEditor – AAPI journal (American Association of Physicians of Indian Origin. Circulation -40,000)\n●\tVisiting Professorship in Hematology Oncology and Stem Cell Transplantation, Rajasthan University of Medical Sciences, India. ( 2009-Ongoing )\n●\tIndustry Advisor – Bayer, UniQure, Sanofi-Genzyme, Takeda, CSL Behring\n●\tDirector of International Bone Marrow Failure Consortium- India, part of the Global Hematology Initiative of Cohen Children’s Medical Center, New York, August 2015-2017. \n●\tCommittee member for the American Society of Pediatric Hematology Oncology Special Interest Group in Global Pediatric Hematology oncology. ( 2016- Ongoing)\n\n\n WORK EXPERIENCE \nNov 2017- Current Loma Linda University Children’s Hospital \n Director Division of Pediatric Hematology \n Director, Comprehensive Hemophilia Program\n Director, Comprehensive Sickle Cell Program \n Division of Pediatric Hematology Oncology and Stem Cell Transplantation\n Professor of Public Health, Loma Linda University School of Public Health \n\nMar 2017– Oct 2017 Pediatrics and Pediatric Hematology Oncology Practice \n Adventist Health Ukiah Valley, California \n\nSept 2015 –Aug 2016 Assistant Professor Pediatrics, Hofstra North Shore LIJ School of Medicine \n Section Head –Global Pediatric Hematology Oncology and Stem Cell Transplantation\n North Shore LIJ Health system.\n Associate Adjunct Faculty, Hofstra University School of Public Health.\n\nJuly 2012 – Sep 2015 The Steven and Alexandra Cohen’s Children's’ Hospital of New York at LIJ-North Shore \n Hofstra University - Pediatrics Hematology Oncology and Stem Cell Transplant Fellowship \n Chief - Jeffrey Lipton MD\n\nDec 2011- April 2012 Global Health : SMS Medical College and Group of Hospitals, Government of India \n Project Director for Project A.G.N.I. - Set up a regional Lead Poisoning prevention and \n anemia nodal center \n \n Course Director - Pediatric Subspecialty training module for Pediatricians at J.K. Lone \n Children’s Hospital for Government of India. \n\nDec 08- Dec 2011 The New York Medical College, Residency in Pediatrics \n Metropolitan Hospital, NY\n Maria Fareri Children's Hospital at Westchester.\n The Memorial Sloan Kettering Hospital. NY\n House staff on Stem Cell Transplantation service.\n \nApril – August 2008 Oklahoma State Medical Association (O.S.M.A.) Externship Program\n The Integris Baptist Teaching Hospital and Nazih Zuhdi Transplant Center\n\nRESEARCH EXPERIENCE \nNov 2017 – Ongoing: Current and ongoing – Director, Inherited Bleeding Disorder Experimental Therapeutics Program, Loma Linda University School of Medicine\nJan 2014 –July 2015 - Hofstra University School of Public Health \n Needs Assessment to barriers in cancer care for newly diagnosed patients in a resource \n Limited setting. \n Principal Investigator - Akshat Jain, Co-PI -Corrine Kyriacou \n\nJune 2012- July 2015 - Steven and Alexandra Cohen Children’s Medical Center \n Study – Non Invasive assessment of endothelial dysfunction in children with Sickle cell \n Disease. \n Co-Principal Investigator – Banu Aygun MD\n Study – Multicenter study assessing outcome of Reduced Intensity Conditioning for \n patients undergoing hematopoetic stem cell transplantation for Sickle cell disease . \n Co-Principal Investigator – Indira Sahdev MD\n \nJan 2012- Mar12 A.G.N.I. (Anterograde Growth Normalization Initiative) \n Project Director, Project of Government of India for establishment of Universal Lead \n Independent Pilot project to study effects of Elevated Blood Lead levels in children \n suffering from Developmental disorders- Adapted by W.H.O. 2014 for a National Level \n Lead Screening program, India \n \nJan 2009- Dec11 The New York Medical College, Metropolitan Hospital Center. NY\n Resident Physician – Hypothalamic volumes in patients with Growth Hormone deficiency.\n Maria Fareri Children's hospital / Dr.Richard Noto - Pediatric Endocrinology\n \nApril 2008-Dec 08 Nazih Zuhdi Transplant Institute, Integris Baptist Hospital, Oklahoma City\n Project – Single institution outcome study for Solid organ transplants\n Research Assistant Department of Hepatology\n \nOct 2007 – Dec07 Mount Sinai School of Medicine, New York, NY\n Project- Arterio-venous fistula post liver transplantation.\n Research mentor-Dr. Charissa Chang, Assistant Professor in Department of Liver Diseases. \n\nCERTIFICATION\n\n1.\tCalifornia State Medical License 8/2016- Present , New York State Licensure 8/2013-12/16\n2.\tAmerican Board of Pediatrics - Board certified, 11/14- Present\n3.\tAmerican Board of Pediatric Hematology Oncology – Board Certified , 06/2018- Present\n4.\tNeonatal Advanced Life Support 06/2009-Present \n5.\tPediatric Advanced Life Support 06/2009-Present \n6.\tECFMG Certification 12/2007-Present \n\nORAL PRESENTATIONS \n\n\n1.\tLeukemia and Lymphoma Society of America C.M.E. Symposium presentation – Leukemia and Beyond: Advances in Cancer Care and Blood Disorders in the 21st Century, October 2019\n2.\tLoma Linda University School of Medicine – Grand Rounds, Advances in the Management of Sickle Cell Disease, March 2019.\n3.\tLoma Linda University School of Medicine – Experimental Therapeutics in Sickle Cell Disease – New Horizons at Loma Linda , November 2018 .\n4.\tAdventist Health Ukiah , California - Neurological Defects of Iron Deficiency and Lead Poisoning in Humans , October 2017\n5.\tHofstra NorthWell School of Medicine - National Public Health Symposium on Global Public Health , Convener and Moderator ,April 2016 \n6.\tCleveland Clinic Children’s Medical Center, Ohio – Non BCR-ABL Myeloproliferative syndromes of childhood, January 19, 2016.\n7.\tChildren’s Hospital at SMS Medical College ,India – Pediatric Hematology Oncology Emergencies for the Tropics, November 13, 2015 \n8.\tHarvard Medical School, Boston Children’s Hospital Division of Pediatric Hematology – Advances in Global Hematology, Annual Hemophilia Twining symposium, August 2, 2015.\n9.\tNew York Medical College as Grand Rounds, Division of Pediatrics – Emergencies in Pediatric Hematology and Oncology, April 2015.\n10.\tMaurice A. Deane School of Law, Hofstra University, New York - Healthcare Access to Undocumented immigrants: Immigration reform and its impact, March 2015.\n11.\tPediatric Academic Society/Society of Pediatric Research (PAS/SPR) as platform presentation, Vancouver, BC - Global Child Health in Rich & Poor Countries Lessons Learned from Indigenous Health, May 3 2014.\n12.\tDepartment of Medicine and Medical Oncology, as Guest International faculty , SMS Medical College, India - Advances in Stem Cell Transplantation – January 2014.\n13.\tInternational health conference, Global Association of physicians of Indian Origin , New Jersey – Impact of Lead Intoxication in Low to middle income countries , August 2012.\n14.\t139st APHA Annual Meeting and Exposition 2011, Boston - Use of decision support in a Harlem pediatric emergency department to increase prescription of controller medicines to patients with poorly controlled asthma - Wilson Wang, Carolina Valez, Nicole Falanga, Vikas Bhambhani , Akshat Jain , Farhad Gazi, David Spiller, Paper no-227188 , November 2011 \n15.\tThe New York Academy of Medicine, Resident award night - False negative result in newborn screening for Congenital Adrenal hyperplasia - July 2009.",institutionString:"Loma Linda University Children's Hospital",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Loma Linda University Children's Hospital",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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The reader antenna is an important unit of RFID systems. Many new RFID antennas are developed for different applications [3–7]. Reader antennas can be classified into two types based on the working scope for different application purposes: near-field antenna and far-field antenna. Currently, ultra-high frequency (UHF) near-field RFID technology are fast developed in item-level identifications such as sensitive products tracking, biological products and medical products (blood, medicines, vaccines), biosensing applications and so on [8–12].
\nThe basic consideration of UHF near-field RFID is to make it work in a short distance stable, just like what LF/HF near-field RFID does [8]. Inductive coupling systems are selected in most applications in near-field UHF RFID, because most of the reactive energy is stored in magnetic field. Inductive coupling is more stable than the capacitive coupling and hardly affected by liquid or metal [13].
\nIn a near-field RFID system, the reader and the tag antennas are coupled mainly through magnetic field. If the tag antenna is electrically small, the magnetic field of reader antenna is perturbed by tag rarely, and coupling coefficient C could be shown by the equation [14, 15].
\nwhere
One of the challenges in UHF near-field RFID applications is to design a reader antenna with wide bandwidth and strong near magnetic field simultaneously. Strong near magnetic field is important for extending the reading range. Low gain could reduce interferences. Wide bandwidth antenna could be applied at 840–960 MHz, which covers both ETSI of Europe and FCC of North America. In some special application scenarios, smaller size antennas are required because of the limited system space.
\nSome near-field antennas have been reported to generate strong and even magnetic field. Many travelling wave antenna is proposed to extend the bandwidth [16]. A conventional travelling wave antenna called Mini-Guardrail from a famous company Impinj has wide bandwidth of more than 200 MHz at UHF band as well as low gain [17]. But because, loads of traveling wave antenna consume too much power, current and near magnetic field are not strong enough. The magnetic field should be lower than −13 dBA/m at any direction if the distance above antenna is larger than 2 cm. Conventional standing wave antenna could present a strong magnetic field, but its gain is too high and bandwidth is too narrow to cover a wide UHF RFID bandwidth. Typical standing wave antenna with strong magnetic field like eye shape is proposed by Li et al. [18], but −10 dB bandwidth is only 30 MHz which could only cover FCC band. Narrow bandwidth antenna is easily detuned by environmental changes.
\nThe current distribution of the near-field antenna is in-phase. That is, the current on the loops has the same orientation. For example, a two-layer and open circuit shape near-field antenna made by PCB board is shown in \nFigure 1\n. The current character is explained by this example.
\nModel and structure of the proposed antenna: (a) 3D view, (b) top view and (c) side view.
\n\nFigure 1(a)\n shows the structure of the antenna, which is composed of two PCB layers. The top layer is mainly composed of two quasi-half loops which are connected with two folded straight terminals. The bottom layer is feed network with ground and lead are printed onto top and bottom surface, respectively. Fed at edge of bottom layer, 50 ohm microstrip line is connected with two 100-ohm lines, which are connected to two metal columns in another end. The other end of each column is connected to metal quasi-half loop strip. There are four loads on two quasi-half loops which are marked by 1, 2, 3 and 4 in \nFigure 1(a)\n. The two PCB boards are connected by two metal columns and fixed by two nylon columns.
\n\n\nFigure 1(b)\n shows the top view. Angle and radius of loop are marked by
The proposed antenna can be printed onto any substrate and optimized at specific operating frequency by properly selecting the geometrical parameters. The antenna prototype is printed onto a FR4 substrate (
Photograph of the antenna: (a) 45° view of the antenna and (b) bottom view of the antenna.
The impedance matching measurement of the antennas was carried out using the Agilent N5230A vector network analyzer. \nFigure 3\n shows the simulated and measured return loss. The proposed antenna exhibits broadband impedance bandwidth, the frequency range for −15 dB return loss is from 826 to 950 MHz or 124 MHz bandwidth. The measured result agrees well with the simulation.
\nS11 of the actual antenna and simulation model.
The far-field gain and directivity are shown in \nFigure 4\n. It is clear that the gain is lower than −10 dB at any direction and about 15 dB lower than directivity.
\n(a) Far-field gain versus theta at different Φ, which is 0, 90, 180 and 270°, and marked by four curves; (b) directivity versus theta at different Φ which is same as (a).
\n\nFigure 5\n shows the current distribution of different phases. Different from conventional traveling wave antenna, current on the loop assumes standing wave distribution. One important factor of current is in-phase. Because magnetic field produced by the currents on the adjacent sides of the antenna cancel out each other and is thus very weak in the central portion of interrogation zone if the current distribution is out-phase. In \nFigure 5\n, current of this antenna is not out-phase because of small electrical length of the quasi-half loop. At the phases of 45, 90 and 135 degree, the currents are all strong. Actually, the magnetic field at these phases are also strong. The result could be verified by the comparison between \nFigures 5\n and \n6\n.
\nCurrent distribution on metal strip at different phases: (a) 0°, (b) 45°, (c) 90° and (d) 135°.
Different phases of
\n\nFigure 6\n shows that magnetic field is concentrated and uniform around the center region of antenna at different phases (0°, 45°, 90°, 135°). Moreover, as a result of folded terminal, average current can be enhanced on the outer loop so that strong magnetic field intensity is obtained.
\n\n\nFigure 7(a)\n shows the magnetic field distribution at different
Simulated magnetic field distribution of the antenna operated at 868 MHz: (a)
Compared to other antenna, it could be found that the
To further verify the performance of the proposed two-layer and two quasi-half loops antenna, the prototype was used as the reader antenna in a UHF near-field RFID system to detect UHF near-field tags. Test system is shown in \nFigure 8(a)\n, and the proposed antenna was connected to the reader operating at both 865–868 MHz of ETSI and 902–928 MHz of FCC with 30 dBm output to detect tag. Tag is positioned on a foam board, which has a size of 70 mm×70 mm, could be shown as \nFigure 8(b)\n. Grid is marked on the top of the foam board with the size of 1 cm×1 cm. The data of detected tag on each intersection were recorded.
\nThe configuration of reading range measurement. (a) Configuration of measurement scenarios: reader antenna, circulator and reader, which are connected with computer. (b) Foam board with tag is positioned above the antenna.
This chapter adopts one annular tag which could be activated when magnetic field intensity is stronger than −13 dBA/m. The system choose ETSI band as the operating frequency because the operating mechanism of FCC is hopping frequency (HF).
\nThe measurement results of reading range are exhibited in \nFigure 9\n; it is clear that reading scope is reduced if distance increased. Compared between \nFigures 9\n and \n7(a)\n, it could be found that at each
Reading scope of antenna at different distances, which are 1, 2, 3 and 4 cm. Black grid marked the intersection which could be read when tag is put on it, and white grid marked the opposite.
The antenna’s structure is a distorted symmetrical dipole. So, the length of metal loops and the input impedance could be estimated based on dipole theory. If resonant frequency is determined, the electric length of a metal column, quasi-half loop and folded terminal should match the resonant frequency.
\nwhere
After extensive simulations, it is found that loads (
\n\nFigure 10(a)\n and \n(b)\n shows the magnetic field distribution of antennas at different values of
The relationship between loads and magnetic field at 868 MHz: (a)
\n | \nS11 at resonant frequency | \n−15 dB bandwidth | \n−10 dB bandwidth | \n
---|---|---|---|
10 ohm | \n−11.95 dB at 884 MHz | \n0 MHz | \n61 MHz (859–920 MHz) | \n
30 ohm | \n−33.5 dB at 884 MHz | \n123 MHz (828–951 MHz) | \n190 MHz (808–998 MHz) | \n
60 ohm | \n−13.81 dB at 884 MHz | \n0 MHz | \n179 MHz (818–986 MHz) | \n
100 ohm | \n-8.35 dB at 893 MHz | \n0 MHz | \n0 MHz | \n
Relationship between the Res1, resonant frequency and bandwidth.
\n | \nS11 at resonant frequency | \n−15 dB bandwidth | \n−10 dB bandwidth | \n
---|---|---|---|
10 ohm | \n−16.7 dB at 877 MHz | \n42 MHz (857–899 MHz) | \n124 MHz (817–941 MHz) | \n
30 ohm | \n−21.7 dB at 879 MHz | \n72 MHz (844–916 MHz) | \n145 MHz (810–955 MHz) | \n
50 ohm | \n−33.5 dB at 884 MHz | \n123 MHz (828–951 MHz) | \n190 MHz (808–998 MHz) | \n
90 ohm | \n−25.4d B at 894 MHz | \n96 MHz (850–946 MHz) | \n184 MHz (811–996 MHz) | \n
Relationship between the Res2, resonant frequency and bandwidth.
Both magnetic field and return loss are more sensitive to
\n\nFigure 11(a)\n and \n(b)\n exhibits the magnetic field distribution of the antennas with varying heights of
The relationship between the
Radius of quasi-half loop is also an important influencing factor of magnetic field. In the experiments, other parameters are unchanged except
Magnetic field distribution of different
\n\nFigure 13(a)\n shows the structure of the antenna. The top layer contains two quasi-half loops with two inductance-like terminals. The bottom layer is the feed network with the ground and lead. Fed at the edge, a 50 ohm microstrip line is connected to two 100 ohm microstrip lines which connect metal columns. The other end of each column is connected with a metal loop strip. On loops, there are four printed loads which are marked by 1, 2, 3 and 4 in \nFigure 13(a)\n. The two PCB boards are supported by two nylon columns.
\nModel and structure of the proposed antenna: (a) 3D view, (b) top view and (c) side view.
\n\nFigure 13(b)\n shows the top view of antenna. The angle, width and radius of the quasi-half loop are marked by
It is challenging to design UHF near-field RFID antennas with strong magnetic fields and broad bandwidth. The UHF RFID antenna is demonstrated to be able to achieve a broad bandwidth and a low gain. The proposed antenna has also been proven to have strong magnetic fields with concentrated field distribution in the near-field region of antenna, which is very suitable for UHF near-field RFID reader applications.
\nMoreover, the investigation has shown that the UHF RFID antenna has produced the stronger magnetic field distribution. The most impact factor for near-field RFID antenna is magnetic field distribution. In recent years, near field radio frequency identification system has developed, many antennas are developed, but there are still have some problems. The combination of magnetic field construction and band widening technology is not perfect, especially the low-loss large-scale standing wave near-field antenna band widening technology is rarely reported.
\nThe near-field theory is the research foundation of the near-field antenna. The breakthrough of the near-field electromagnetism theory and its innovation not only play an extremely important role in the development of the electromagnetism itself but also promotes the radio frequency identification directly and also a series of modern electronic technology development, so the analysis of the near-field problem is very basic in the electromagnetism, valuable and challenging research work.
\nA concussion is defined as a mild traumatic brain injury induced by biomechanical forces, which results in an array of signs and symptoms that can include somatic, cognitive, behavioral or emotional changes, sleep disturbances and/or balance problems [1]. Most concussions resolve spontaneously, but some studies indicate that as many as 43% of individuals continue to experience persistent and disabling impairments months after their injury [2]. Persistent post-concussive symptoms (PPCS) refer to the lack of clinical recovery within 10–14 days for adults, and within four weeks for children [1]. As described in a recent review article [3], there is a lack of consensus about numerous issues related to PPCS including causation. However, considering predisposing, precipitating, and perpetuating factors appears to be a fruitful approach [4]. Nevertheless, PPCS are problematic because they decrease quality of life. For example, individuals with PPCS have reduced social interactions, difficulty continuing previously enjoyed past-times, and struggle resuming pre-injury physical capabilities, employment, and daily tasks [5]. Driving can also be impacted, with one study reporting that 93% of individuals with PPCS experience at least one difficulty that negatively impacted their driving [6].
Driving requires the integration of motor, cognitive, perceptual, and sensory skills in response to environmental information [7]. Sustaining a concussion may impact these driving abilities and result in impaired driving performance [6, 8, 9]. Furthermore, individuals who experienced a concussion but are no longer symptomatic exhibit impaired driving performance when assessed in a driving simulator [10]. Such impairments are also evident in on-road driving, where the number of motor vehicle collisions for persons six to nine years following a traumatic brain injury are more than double the reported average [11]. Accordingly, treatments are necessary to reduce the risk while driving following a concussion.
There are several challenges to treating individuals with PPCS. Individualized treatment plans that target physical and psychosocial symptoms are recommended [1, 12]. However, treatments focused on symptoms do not necessarily address their root cause, which may be altered brain physiology. Biofeedback approaches are designed to address physiological injury and may improve functional performance [13].
Heart rate variability (HRV) describes the natural beat-to-beat variability in heart rate. It represents autonomic function and sympathetic-parasympathetic balance [14]. HRV is altered in individuals suffering concussions [15] and PPCS, including hyperactive sympathetic activity and reduced parasympathetic activity [16]. HRV biofeedback is designed to repair sympathetic-parasympathetic balance, as well as baroreflex activity [17]. It improves cognitive functioning and emotional regulation in some individuals experiencing a brain injury [18]. It may also contribute to improved attention [19] and problem-solving abilities [20], and enhanced executive functioning [21]. HRV biofeedback may also reduce symptoms and improve mood in individuals with PPCS [22, 23].
HRV biofeedback is often used in conjunction with electroencephalograph (EEG) biofeedback (neurofeedback) since neurofeedback can also influence the neuroanatomical networks and structures that affect HRV [13, 24, 25]. Neurofeedback has evolved from measuring and training brain activity using surface electrodes, to more robust methods including source localization neurofeedback. This form of neurofeedback is known as low-resolution electromagnetic tomography (LoRETA) neurofeedback [26]. LoRETA neurofeedback allows the participant to see the amplitude of electrical activity at specific brain regions in real time, and they can therefore self-regulate this electrical activity [26]. It is non-invasive and enables individualized rehabilitation. Individualization is important as it is one of the biggest limitations of traditional brain injury interventions [12].
LoRETA neurofeedback corrects functional deficiencies in individuals with major depressive disorder [27]. When combined with HRV biofeedback, the intervention improves both depression and anxiety symptoms [28], which may contribute to improved driving performance and reduced driving errors [29, 30]. Additionally, the combined intervention may help individuals perceive, attend, and interpret a stimulus [7] by improving disorders of attentional processing [31, 32]. Following the interpretation of a stimulus, the driver must plan an action to react to a stimulus, and then execute the action [7]. LoRETA neurofeedback and HRV biofeedback may improve planning by improving executive function [32], and may improve execution by increasing motor [33] and impulse control [31]. Planning an action can also be improved through previous experience [7], and this may be improved through LoRETA neurofeedback and HRV biofeedback by way of improving working memory [34].
Therefore, this chapter describes a research study designed to determine whether HRV biofeedback in combination with LoRETA neurofeedback showed promise as an intervention to reduce self-reported concussive symptoms and improve simulated driving performance in individuals experiencing PPCS.
Thirty-one individuals were recruited to participate in this study, which was approved by the Western University Health Science Research Ethics Board and registered with ClinicalTrials.gov (NCT03338036). Participants with PPCS had to be 18 years of age or older, experienced a clinically diagnosed concussion and completed a concussion rehabilitation program, and still experiencing ongoing symptoms. They also had to be fluent in English, hold a valid driver’s license, and capable of using hand-held devices. Healthy participants had to be 18 years of age or older, and could not have experienced a concussion in the last two years. They also had to be fluent in English and hold a valid driver’s license. All participants provided written informed consent.
Twenty-three individuals with PPCS were randomized into the intervention or active control group (11 in the intervention group and 12 in the PPCS control group). However, seven PPCS participants experienced a worsening of symptoms during the baseline testing and could not complete the driving simulator task, excluding them from participation. This resulted in seven participants in the intervention group (48.6 ± 14 years old, four females). The youngest participant in the intervention group was 30, while the oldest was 75. The PPCS control group had nine participants (54.7 ± 8 years old, six females), with the youngest being 37 and the oldest being 65. Lastly, there were eight healthy control participants (49.6 ± 16.5 years old, four females). The youngest healthy control participant was 25 while the oldest was 74.
Participants were initially contacted via email about this study; their response prompted an informational email. They then met with a study investigator at the iMobile Research Lab at Western University, London, Ontario, Canada, where together they reviewed the letter of information. Once all questions were answered and they signed the consent form, the baseline assessment began.
The participant was first measured and fitted with a 19-lead EEG cap (Electro Cap International, Eaton, Ohio). Each electrode placement corresponded to specific locations on the scalp according to the 10–20 International System for electrode placement [35]. The electrodes were then filled with a water-soluble conducting gel (Electro-Gel, Electro Cap International, Eaton, Ohio). An abrasive gel (NuPrep) was used as skin preparation prior to attaching electrodes to both earlobes using clip electrodes; these sites acted as a reference. All leads used AFz as ground and passed through an amplifier (Evoke Neurosciences, New York, NY). Additionally, one electrode was taped to the participants chest, inferior to the left clavicle, to monitor their electrocardiogram.
The participant then completed a brain function assessment, including a three-minute resting EEG measurement with their eyes-closed. Afterwards, the participant completed a Rivermead Post-Concussion Symptoms Questionnaire (RPQ) [36] and Generalized Anxiety Disorder 7-Item Scale (GAD-7) [37]. Next, they performed the driving simulation task on a CDS-200 DriveSafety™ simulator, which included a steering wheel and dash display from a Ford Focus, a gas and brake pedal, and three computer screens for displaying the environment around the vehicle. The simulator was adjusted for the participant’s comfort, ensuring that they were the appropriate distance from the screens, and they were comfortable with the height and tilt of the steering wheel and distance to the pedals.
The simulation task began with a simulator acclimation protocol including dimmed lights to reduce visual strain, temperature control in the simulator room (21° C) to ensure comfort, utilization of a fan to increase air flow around the participant, and three acclimation drives totaling seven minutes. These factors have been identified to mitigate simulator sickness [38]. The acclimation drives increased in complexity, starting with a straight drive while maintaining a speed of approximately 50 kph with no other vehicles on the road and low visual complexity of the scenario. The next acclimation drive required navigating a city block with four consecutive left-hand turns, and ended with a drive requiring four consecutive right-hand turns. The left-hand and right-hand turn scenarios were completed with few vehicles on the road, thus introducing real driving situations. For example, the participant had to wait for an oncoming car to drive through the intersection before completing a left-hand turn. Participants were offered breaks between simulator tasks as needed. They were also screened for symptoms of simulator sickness before and after each acclimation drive using the Adapted Motion Sickness Assessment Questionnaire [39], adapted to an 11-point scale as done in previous research [40]. Participants rated their feelings of sweatiness, queasiness, dizziness, and nausea on a scale from 0 (not at all) to 10 (severely).
Finally, participants performed one of two simulator drives. Both drives contained the same scripted events representing potentially hazardous situations: an unexpected pedestrian crossing the street in front of the car, and a car suddenly pulling out of a driveway in front of the participant. The scripted events were pseudorandomized across the two drives to control for any potential learning effect of the route. The drive was approximately 10 minutes in length. These drives have been used in other experiments, and were specifically designed to assess the driving performance of young adults [40, 41].
After eight weeks, all participants returned to complete another brain function assessment, RPQ and GAD-7, and driving simulator acclimation and drive. The final simulator drive was the alternate drive to their baseline assessment. For example, if they completed Drive 1 in their baseline assessment, then they completed Drive 2 in their follow-up assessment.
Participants in the intervention group received an Android tablet (either a Craig 7 inch 1 GB 6.0 “Marshmallow” Tablet, New York, New York or a Samsung Galaxy Tab A 7 inch 8 GB Android 5.1 “Lillipop” Tablet, Seoul, South Korea) and heart rate variability training tool (Evoke Waveband, Evoke Neurosciences, New York, New York) upon completion of their initial assessment. Participants in the intervention group were taught how to use the equipment, and instructed to perform a HRV biofeedback session every morning and night for eight weeks. Each HRV biofeedback session involved placing the Waveband just below their elbow, opening the application (Mindja, Evoke Neurosciences, New York, New York) on their tablet, and doing a 5-minute exercise in which they were cued to breathe at their resonant frequency [42]. Points were awarded as their HRV improved. Participants were also provided with a log book to record the dates and times of their completed sessions.
LoRETA neurofeedback sessions were performed in a private room at Parkwood Institute in London, Ontario. Each LoRETA neurofeedback session was broken up into 10 exposures, each two-minutes in duration, for a total of 20 minutes of training. Participants were instructed to “relax, focus, and turn on the green light”, which would appear on a computer screen in front of them. The light turned green when the participants were appropriately activating the target cerebral areas at the appropriate amplitude, as determined from their initial assessment. Each participant in the intervention group was scheduled to participate in three sessions per week (usually at the same time on Mondays, Wednesdays, and Fridays), for eight consecutive weeks. This resulted in a potential total of 24 LoRETA neurofeedback sessions and 112 HRV biofeedback sessions.
Total scores on the GAD-7 for each participant were summed, and the change from baseline to follow-up was calculated. These changes were compared between the intervention, PPCS control, and healthy control groups using a Kruskal-Wallis non-parametric analysis (SPSS 25, IBM Corp., Armonk, NY). RPQ outcomes were tallied as two scores, similarly to previous research [43]. The headache, nausea and dizziness scores were tallied together (RPQ-3), and the remaining questions were tallied separately (RPQ-13). The differences from baseline to follow-up between the three participant groups in both RPQ sub scores were also assessed using a Kruskal-Wallis analysis.
Driving simulation analysis focused on two scripted events (an unexpected pedestrian crossing and a car suddenly pulling out of a driveway), as these events challenged the participant’s reactions. Three parameters were assessed for these events: reaction time, maximum brake applied and the distance from the event when the maximum brake was applied. Reaction times were quantified as the time difference between the start of the hazardous event and when the participant applied pressure to the brake or suddenly changed their lane deviation (i.e. swerving). Maximum brake applied was indicated on a zero to one scale, with zero representing no braking and one representing the maximum brake application possible. Differences from baseline to follow-up between the three participant groups were analyzed using a non-parametric Kruskal-Wallis analysis.
Participants in the intervention group attended 88% of their LoRETA neurofeedback sessions (21 ± 2.6 of the 24 possible sessions; the 25, 50, and 75th percentiles were 18.5, 22 and 23, respectively). The range extended from a low of 17 (one participant) to a maximum of 24 (two participants). Additionally, participants on average completed 86% of their HRV sessions (96.7 ± 10.1 of the 112 possible sessions; the 25, 50, and 75th percentiles were 86, 99, and 106, respectively). The range extended from a low of 83 (two participants) to a maximum of 111 (one participant).
There were no significant differences in GAD-7 or RPQ-13 between the intervention, PPCS control, and healthy control groups (Table 1). There were significant differences in RPQ-3 outcomes. Post-hoc analysis revealed significant differences between the intervention group and healthy control group (
Outcome | Intervention | PPCS Control | Healthy Control | ||
---|---|---|---|---|---|
GAD-7 (Median) | −2.3 | −0.6 | −1.5 | 0.94 | .62 |
RPQ-3 (Median) | −1 | −3 | 5.5 | 12.02 | <.01* |
RPQ-13 (Mean rank) | 13.2 | 13.0 | 16.2 | 0.88 | .65 |
Car pull out reaction time (Mean rank) | 7.7 | 13.0 | 9.7 | 2.88 | .24 |
Car pull out max brake (0–1; Median) | −.14 | 0.00 | −.05 | 0.97 | .62 |
Car pull out distance at brake max (m; Median) | 2.47 | −2.70 | −0.65 | 3.66 | .16 |
Pedestrian walk out reaction time (s; Median) | −0.48 | 0.53 | −0.08 | 1.19 | .55 |
Pedestrian walk out max brake (0–1; Median) | 0.00 | −.03 | −.03 | 0.02 | .99 |
Pedestrian walk out distance at brake max (Mean rank) | 12.3 | 10.0 | 9.1 | 1.05 | .59 |
Statistical evaluations of the change in generalized anxiety disorder 7-item scale (GAD-7), Rivermead post-concussion symptoms questionnaire (RPQ), and driving outcomes from baseline to follow-up. *indicates statistical significance.
GAD-7 scores for individual participants. Green indicates participants in the intervention group, gray indicates PPCS controls, and black indicates healthy controls.
RPQ-3 scores for individual participants. Green indicates participants in the intervention group, gray indicates PPCS controls, and black indicates healthy controls.
RPQ-13 scores for individual participants. Green indicates participants in the intervention group, gray indicates PPCS controls, and black indicates healthy controls.
Ten participants were involved in a collision during their driving simulator performances. Eight collisions occurred in the baseline assessment (two from the intervention group, two from the PPCS control group, and four from the healthy control group) and two occurred in their follow-up assessment (both from the PPCS control group). Collisions automatically terminated the simulation. Two collisions occurred after the scripted events. Accordingly, full drive metrics were not available for eight participants. The analysis of changes in reaction time to the car suddenly pulling out of the driveway were based on seven intervention participants, six PPCS control participants, and six healthy controls. There were no differences between groups in reaction time to the car suddenly pulling out, or their maximum brake effort or distance (Table 1).
Collisions also effectively reduced the number of participants that were exposed to the unexpected pedestrian crossing. The change in reaction time in response to the unexpected pedestrian crossing was based on six intervention participants, seven PPCS control participants, and eight healthy controls. There were no differences between groups in their reaction time, maximum brake effort or distance to an unexpected pedestrian crossing (Table 1).
Our intervention evaluated a combination of LoRETA neurofeedback and HRV biofeedback in individuals with PPCS, and compared concussive symptoms and driving performance to participants with PPCS that did not receive the intervention, and a healthy control group. Individuals in the intervention group reported improvements in RPQ-3 outcomes compared to the healthy control group. However, the PPCS control group also had reduced RPQ-3 outcomes compared to the healthy control group, and the difference between the intervention and PPCS control group was not significant. There were no statistically significant differences in GAD-7 scores or driving simulation outcomes (reaction time, maximum brake or distance at maximum brake) to the scripted events of the car suddenly pulling out of a driveway or the unexpected pedestrian crossing. Therefore, the results of this study indicate that LoRETA neurofeedback and HRV biofeedback did not reduce symptom number or severity, nor did it improve simulated driving performance. However, outcomes varied between participants. Emerging models evaluating the predisposing, precipitating, and perpetuating factors related to PPCS [4] may provide insights into the variable responses between participants, and should be considered in future investigations.
Previous research has shown that a single session of LoRETA neurofeedback results in acute changes in current densities in specific regions of the brain [27]. As well, previous intervention studies have indicated that LoRETA neurofeedback reduced symptoms in individuals with a brain injury [44, 45]. But, these studies tailored the LoRETA neurofeedback protocol for each individual. Furthermore, the target cerebral areas and training amplitudes also varied between studies based on the individual initial assessments. Despite our utilization of an initial EEG assessment, other factors could have been considered to inform cerebral area and amplitude selection, and further individualize our protocol.
Previous research has recommended considering initial symptom presentation when creating a LoRETA neurofeedback protocol to treat acute brain injuries [46]. Initial symptom presentation was difficult to attain in our study because participants were no longer in the acute phase of their injury. Therefore, this increased the risk of recall bias because of this population’s increased likelihood for impaired memory recall [47]. However, it is unclear whether considering initial symptom presentation, current symptom presentation, or a combination may be more appropriate when creating a LoRETA neurofeedback protocol for individuals with PPCS. Secondly, the number of sessions should be based on the rate of improvement rather than a fixed number of sessions [46], which we chose to maintain for a consistent research protocol. The 24-session protocol was recommended by a licensed practitioner to balance intervention effects, research feasibility, and participant compliance. Lastly, consideration of persisting symptoms and their severity may further refine the intervention to increase participant’s improvements. For example, there is a hyperbolic relationship between initial symptom presentation and number of neurofeedback sessions necessary [46], with increased initial symptoms requiring more neurofeedback sessions. Similarly, an increase in symptom persistence could increase the number of neurofeedback sessions required for improvement. Consideration of these three factors may have influenced the participants’ responses to the intervention.
The lack of improvement in the driving simulation may have also been a result of the complex relationship between the outcome measures in this study. Outcomes (symptoms and driving simulation metrics) were evaluated independently, but there may not be a direct relationship between symptoms and driving simulation performance. Other studies have used structural equation modeling to evaluate similar types of relationships [48], but this was not possible in the current experiment due to the limited number of participants.
Individuals with a concussion or PPCS often experience executive dysfunction [47], which may be exhibited by inappropriate driving speed, following others too closely, or braking at inappropriate times [49]. Participants in the intervention group exhibited the greatest decreases in reaction times to the hazardous events (pedestrian walking out and car pulling out). Although these changes were not statistically significant, improved reaction times may indicate improved processing times, which is associated with fewer collisions [50]. Additionally, participants in the intervention group had the largest increases in distance from the hazardous event when they maximally applied the brake. They also exhibited the largest decrease in maximum brake effort when the car pulled out (although not statistically significant). These improvements also indicate increased driving safety, as increased distance and decreased brake effort indicate improved decision-making and ultimately improved executive function [49]. These safety improvements are particularly important for the PPCS population as their risk of collision may be higher when compared to the normal population [51].
The results of this study further indicate that individuals with PPCS require specialized driving evaluations, as previously identified [6, 9, 52, 53]. However, only half of physicians ‘almost always’ provide driving guidance following a concussion [54]. The lack of universally accepted procedures may be related to the absence of return-to-drive guidelines. Current clinical practice guidelines suggest that individuals who experience a concussion should not drive for 24 hours post-injury [55]. However, there are no guidelines for individuals driving with persistent symptoms, nor graded return to driving. This absence of clinical guidelines to assist physicians in making fitness to drive determinations in this population may increase the risk of collisions in individuals with PPCS. Additionally, 30% of physicians have stated that they do not have clear ‘return-to-drive criteria’ when evaluating fitness to drive in recently concussed individuals [54]. This further illustrates the need for research on the driving performance of individuals with PPCS, which can inform evidence-based return-to-drive guidelines. As indicated by this study, driving simulation research in this population is limited by the likelihood of simulator sickness, as occurred with seven of our recruited participants. This represents a barrier for completing this type of research. However, simulator sickness mitigation protocols can help reduce the incidence and improve retention.
Our study has shown some promising results, but does have some limitations. Most importantly, this study examined the effects of neurofeedback and HRV biofeedback on a small sample size. This represents a challenge with respect to both internal validity and generalizability [56]. For instance, our sample of participants may be biased towards high-functioning individuals that did not experience simulator sickness. Our study also did not consider medication usage that may have influenced participants’ outcome scores [1]. Another potential limitation was that we did not consider driving experience. Although there were no significant differences between age groups, some individuals may have had more experience driving or more driving training. This could have resulted in differences in driving simulation performance prior to a brain injury. Additionally, although our parameterization of the RPQ is similar to previous research [43], other research indicates the RPQ can be quantified using a four factor model, clustered as vision, vertigo, mood/somatic and cognitive domains [57]. It is unclear how our parameterization of the RPQ scores may have influenced the findings. Lastly, this study only looked at the immediate effects of the LoRETA neurofeedback and HRV biofeedback intervention. Although consistent with other neurofeedback studies [31, 58, 59], it is unclear whether short-term responses reflect long-term outcomes. Alternatively, there may be delays before symptoms change [1], and accordingly a reduction in symptoms could also be delayed.
This study is the first to systematically implement and evaluate the outcomes of a LORETA neurofeedback and HRV biofeedback protocol for civilians with PPCS. It is also noteworthy that this study evaluated the outcomes of LORETA neurofeedback and HRV biofeedback in individuals that completed a rehabilitation program and had ongoing PPCS; a population with symptoms that may be difficult to treat [47]. Considering the participant population, these results are especially valuable to healthcare practitioners because they include clinically relevant outcomes (i.e. self-reported symptoms and driving performance).
This study implemented an intervention involving a combination of LoRETA neurofeedback and HRV biofeedback for eight weeks, based on individual EEG baseline assessments. Eleven participants with PPCS were included in the intervention group (seven that finished the protocol), 12 in the PPCS control group (nine that finished the protocol), and eight healthy control participants. Considering the PPCS intervention group as a whole, this combined intervention did not improve symptoms or driving simulation performance. However, some of the individuals did show improvements. This may indicate that this intervention is effective for a subgroup of individuals with PPCS, or perhaps that the intervention needs to be further individualized to optimize participants’ responses. Specifically, the nature of the symptoms, rate of improvement, and length of symptom persistence may need to be considered to individualize the protocol. The results of this study also emphasize the importance of evaluating fitness to drive following a concussion, as well as the need for return-to-drive guidelines for individuals experiencing symptoms following a concussion.
We would like to thank Shannon McGuire, Dalton Wolfe, and the staff of the Acquired Brain Injury Outpatient program at Parkwood Institute for donating research space and helping to recruit participants.
Dr. James Thompson is the Co-Founder of Evoke Neuroscience. Evoke Neuroscience donated the eVox EEG systems for this research, and the corresponding analyses. Dr. Thompson contributed to the research design, analysis and manuscript editing. All remaining authors have no conflict of interest to report.
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\n\nQUALITY CONTENT
\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
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