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Τετάρτη 28 Ιουνίου 2017
The Influence of Intraoperative Testing on Surgical Decision-making During Cochlear Implantation.
Objective: To review our use of intraoperative testing during cochlear implantation (CI) and determine its impact on surgical decision-making. Study Design: Retrospective chart review. Setting: Tertiary referral center. Patients: A total of 197 children and adults who underwent a total of 266 primary and/or revision CI by a single surgeon from 2010 to 2015. Intervention: Intraoperative electrophysiologic monitoring including evoked compound action potentials and electrical impedances. Main Outcome Measures: Whether surgical management was changed based on intraoperative testing. Results: In only 2 of 266 patients (0.8%), the back-up device was used due to findings on intraoperative testing. In three patients (1.1%), X-ray was performed intraoperatively to confirm intracochlear electrode placement, which was found to be normal in all patients. Conclusion: Our data suggest that with respect to CI in children and adults in straightforward cases (e.g., normal anatomy, nondifficult insertion, etc.), routine intraoperative evoked compound action potentials, impedances, and imaging rarely influence surgical decision-making in our clinic and may have limited usefulness in these patients. The implications of this are discussed and a review of the literature is presented. Copyright (C) 2017 by Otology & Neurotology, Inc. Image copyright (C) 2010 Wolters Kluwer Health/Anatomical Chart Company
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Outcomes and Time to Emergence of Auditory Skills After Cochlear Implantation of Children With Charge Syndrome.
Objective: Review perioperative complications, benefits, and the timeframe over which auditory skills develop in children with CHARGE syndrome who receive a cochlear implant (CI). Study Design: IRB-approved retrospective chart review of children with CHARGE syndrome who had at least 12 months of cochlear implant use. Setting: Tertiary care children's hospital. Patients: Twelve children, seven males and five females. Mean age implant = 3.5 years (1.7-8.2 yr); mean duration follow-up = 4.7 years (1.5-10.1 yr). Intervention: Cochlear implantation. Main Outcome Measures: Auditory skills categorized into four levels, temporal bone imaging findings, perioperative complications, time to emergence of speech perception, expressive communication mode. Results: All children imaged with magnetic resonance imaging had cochlear nerve deficiency in at least one ear. Speech awareness threshold improved with the CI compared with aided preoperative in 83% of children, with means of 51.7 dB SAT preoperative and 27.1 dB with the CI (p
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The Hannover Coupler: Controlled Static Prestress in Round Window Stimulation With the Floating Mass Transducer.
Introduction: Stimulation of the cochlear round window (RW) with the floating mass transducer (FMT) still suffers from large variation in clinical outcomes. Beside the geometric mismatch between RW and FMT diameter that is a known limiting factor in achieving optimal coupling between actuator and RW membrane, the applied static force between FMT and RW is usually undefined. In this study, the feasibility and efficacy of a specially designed FMT coupler permitting application of static preloads to the RW membrane to optimize FMT-RW coupling was investigated. Methods: Experiments were performed in fresh human cadaveric temporal bones. The "Hannover Coupler" FMT-prosthesis has a spherical tip (d=0.5 mm) at the front end and a spring at the prosthesis back that enables the application of static preloads and mobility of the FMT at the same time. Stapes footplate (SFP) displacements in response to acoustic stimulation of the tympanic membrane and to RW stimulation by the FMT were measured by a Laser-Doppler vibrometer. Results: Average SFP displacement responses of ASTM standard F2504-05 compliant temporal bones to RW stimulation by the "Hannover Coupler" were dependent on the applied force (~0-100 mN) and increased by up to 25 dB at frequencies >= 1 kHz. When averaged at speech relevant frequencies (0.5, 1, 2, 4 kHz) SFP displacements showed a global maximum at RW preloads of ~4 mN. Conclusion: The coupling between FMT and RW membrane was improved by the application of static RW preloads as indicated by increased SFP amplitudes to RW stimulation. Copyright (C) 2017 by Otology & Neurotology, Inc. Image copyright (C) 2010 Wolters Kluwer Health/Anatomical Chart Company
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Diagnosis of Superior Semicircular Canal Dehiscence in the Presence of Concomitant Otosclerosis.
Objective: To review three patients with concurrent otosclerosis and superior canal dehiscence identified before operative intervention and provide a practical diagnostic approach to this clinical scenario. Study Design: Retrospective patient series. Setting: Tertiary/quaternary referral center. Patients: Individuals with confirmed diagnoses of concurrent otosclerosis and superior semicircular canal dehiscence syndrome. Interventions: Detailed history and physical examinations were performed on these patients, as well as detailed audiovestibular testing and computed tomography imaging. Main Outcome Measures: Establishing a clear diagnosis of concurrent otosclerosis and superior semicircular canal dehiscence syndrome using a thorough diagnostic approach. Results: Three patients presented with conductive hearing loss and normal tympanic membranes. When history and physical examination yielded suspicious third window symptoms/signs, more detailed audiovestibular testing and computed tomography scan imaging were performed. All three patients were ultimately identified to have concurrent otosclerosis and superior canal dehiscence. Conservative management was the option of choice for two of these patients (trial of a hearing aid) and surgical intervention was performed to treat the otosclerosis in the remaining patient. Copyright (C) 2017 by Otology & Neurotology, Inc. Image copyright (C) 2010 Wolters Kluwer Health/Anatomical Chart Company
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Congenital cytomegalovirus infection in pregnancy and the neonate: consensus recommendations for prevention, diagnosis, and therapy.
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Congenital cytomegalovirus infection in pregnancy and the neonate: consensus recommendations for prevention, diagnosis, and therapy.
Lancet Infect Dis. 2017 Jun;17(6):e177-e188
Authors: Rawlinson WD, Boppana SB, Fowler KB, Kimberlin DW, Lazzarotto T, Alain S, Daly K, Doutré S, Gibson L, Giles ML, Greenlee J, Hamilton ST, Harrison GJ, Hui L, Jones CA, Palasanthiran P, Schleiss MR, Shand AW, van Zuylen WJ
Abstract
Congenital cytomegalovirus is the most frequent, yet under-recognised, infectious cause of newborn malformation in developed countries. Despite its clinical and public health importance, questions remain regarding the best diagnostic methods for identifying maternal and neonatal infection, and regarding optimal prevention and therapeutic strategies for infected mothers and neonates. The absence of guidelines impairs global efforts to decrease the effect of congenital cytomegalovirus. Data in the literature suggest that congenital cytomegalovirus infection remains a research priority, but data are yet to be translated into clinical practice. An informal International Congenital Cytomegalovirus Recommendations Group was convened in 2015 to address these questions and to provide recommendations for prevention, diagnosis, and treatment. On the basis of consensus discussions and a review of the literature, we do not support universal screening of mothers and the routine use of cytomegalovirus immunoglobulin for prophylaxis or treatment of infected mothers. However, treatment guidelines for infected neonates were recommended. Consideration must be given to universal neonatal screening for cytomegalovirus to facilitate early detection and intervention for sensorineural hearing loss and developmental delay, where appropriate. The group agreed that education and prevention strategies for mothers were beneficial, and that recommendations will need continual updating as further data become available.
PMID: 28291720 [PubMed - indexed for MEDLINE]
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Recognizing the Needs of Families of Children and Youth Who Are Deafblind.
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Recognizing the Needs of Families of Children and Youth Who Are Deafblind.
Am Ann Deaf. 2016;161(4):454-461
Authors: Correa-Torres SM, Bowen SK
Abstract
Research on deafblindness and families is scant. The few available studies, in combination with research in the areas of visual impairment, hearing impairment, and significant support needs, help paint a picture of the services and other assistance required by families, including siblings of a child with deafblindness. In the present article, the authors synthetize the literature related to families of students who are deafblind and the supports needed by these families. The article also addresses the impact of the diagnosis on the family, supports needed by the family, including the contributions of siblings of the child who is deafblind, and considerations of cultural, linguistic, and economic diversity within the deafblind population. Online resources for professionals who work with students who are deafblind and their families are listed.
PMID: 27818401 [PubMed - indexed for MEDLINE]
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Critical Issues in the Lives of Children and Youth Who Are Deafblind.
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Critical Issues in the Lives of Children and Youth Who Are Deafblind.
Am Ann Deaf. 2016;161(4):406-411
Authors: Nelson C, Bruce SM
Abstract
The coeditors of an American Annals of the Deaf special issue on deaf-blindness introduce readers to critical issues surrounding children and youth who are deafblind. These issues-early identification, communication, social-emotional needs, family and multicultural issues, universal design and assistive technology, transition planning, and personnel preparation-are explored further in the articles that follow. By way of introduction, the present article provides definitions of deafblindness and a discussion of the heterogeneous nature of the population. The history of the field of deafblindness is then explored in terms of three distinct population shifts, from (a) individuals of the 18th and 19th centuries who became deafblind due to illness, to (b) the influx of individuals with congenital rubella syndrome in the 1960s who had disabilities besides deafblindness, and
PMID: 27818397 [PubMed - indexed for MEDLINE]
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A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Rafael Caldas, Marion Mundt, Wolfgang Potthast, Fernando Buarque de Lima Neto, Bernd Markert
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
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Source:Gait & Posture, Volume 57
Author(s): Rafael Caldas, Marion Mundt, Wolfgang Potthast, Fernando Buarque de Lima Neto, Bernd Markert
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
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Cognitive-motor interference in multiple sclerosis: What happens when the gait speed is fixed?
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Ofir Malcay, Yevgenia Grinberg, Shani Berkowitz, Leora Hershkovitz, Alon Kalron
During the last decade, numerous studies have confirmed a coupling between walking performance and cognition in people with multiple sclerosis (PwMS). Our aim was to provide new insights into a walking-cognitive dual-task (DT) in PwMS. We tested the DT phenomenon by controlling the walking speed using an instrumented treadmill. Thirty PwMS (20 women) with a mean age 40.1 (SD=12.0) participated in the study. Twenty-one healthy subjects served as controls. Each subject completed a sequence of tests: a) Normal walking (ST) − the participant walked on the instrumented treadmill at a comfortable walking speed for 1min; b) Cognitive evaluation (ST) − subjects performed two cognitive tests while seated; c) DT cognitive tests performed while walking on the treadmill at the identical speed performed during normal walking. Outcome measures were spatio-temporal parameters of gait (mean and variability), the Word List Generation Test (WLG) and the Serial-3 Subtraction Test. MS participants significantly decreased their cadence while increasing their stride length during the DT condition compared to the ST condition. Non-significant differences were observed for the WLG and Serial-3 Subtraction Cognitive Tests between the ST condition and the DT condition in both the MS and healthy groups. In terms of gait variability parameters, MS subjects demonstrated a 2 to 3-fold greater gait variability compared to the healthy controls. Non-significant differences in gait variability parameters were observed between the ST and DT conditions in both the MS and control groups. This study provides new insights into the DT phenomenon in the MS population.
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Source:Gait & Posture, Volume 57
Author(s): Ofir Malcay, Yevgenia Grinberg, Shani Berkowitz, Leora Hershkovitz, Alon Kalron
During the last decade, numerous studies have confirmed a coupling between walking performance and cognition in people with multiple sclerosis (PwMS). Our aim was to provide new insights into a walking-cognitive dual-task (DT) in PwMS. We tested the DT phenomenon by controlling the walking speed using an instrumented treadmill. Thirty PwMS (20 women) with a mean age 40.1 (SD=12.0) participated in the study. Twenty-one healthy subjects served as controls. Each subject completed a sequence of tests: a) Normal walking (ST) − the participant walked on the instrumented treadmill at a comfortable walking speed for 1min; b) Cognitive evaluation (ST) − subjects performed two cognitive tests while seated; c) DT cognitive tests performed while walking on the treadmill at the identical speed performed during normal walking. Outcome measures were spatio-temporal parameters of gait (mean and variability), the Word List Generation Test (WLG) and the Serial-3 Subtraction Test. MS participants significantly decreased their cadence while increasing their stride length during the DT condition compared to the ST condition. Non-significant differences were observed for the WLG and Serial-3 Subtraction Cognitive Tests between the ST condition and the DT condition in both the MS and healthy groups. In terms of gait variability parameters, MS subjects demonstrated a 2 to 3-fold greater gait variability compared to the healthy controls. Non-significant differences in gait variability parameters were observed between the ST and DT conditions in both the MS and control groups. This study provides new insights into the DT phenomenon in the MS population.
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Accuracy of step count measured by physical activity monitors: The effect of gait speed and anatomical placement site
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Jessica J. Chow, Jeanette M. Thom, Michael A. Wewege, Rachel E. Ward, Belinda J. Parmenter
IntroductionConsumer-based physical activity monitors (PAMs) are becoming increasingly popular, with multiple global organisations recommending physical activity levels that equate to 10,000 steps per day for optimal health. We therefore aimed to compare the step count of five PAMs to a visual step count to identify the most accurate monitors at varying gait speeds, along with the optimal anatomical placement site.MethodsParticipants completed 3min on a treadmill for five speeds (5.0km/h, 6.5km/h, 8.0km/h, 10km/h, 12km/h). An Actigraph wGT3XBT-BT was placed on the waist and wrist, a FitBit One on the waist, and a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on both wrists. A video of participant’s lower limbs was recorded for visual count. Analyses of variance (ANOVAs) were conducted to examine the effects of gait speed and device placement site on step count accuracy.ResultsThirty-one participants (mean age 24.3±5.2yrs) took part. Step count error ranged from 41.3±13.8% for the wrist-worn Actigraph to only 0.04±4.3% and −0.3±4.0% for the waist-worn Fitbit One and Actigraph, respectively. Across all gait speeds, waist-worn devices achieved better accuracy than those on the wrist (p<0.001). The Jawbone was the most accurate wrist-worn consumer-based device at slower speeds (p=0.026), with the Fitbit Flex, and Fitbit Charge HR increasing in accuracy to match the Jawbone at higher speeds.ConclusionThe accuracy and reliability of consumer-based PAMs and the Actigraph is affected by anatomical placement site and walking speed. The Fitbit One and Actigraph on the waist were the strongest performers across all speeds.
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Source:Gait & Posture, Volume 57
Author(s): Jessica J. Chow, Jeanette M. Thom, Michael A. Wewege, Rachel E. Ward, Belinda J. Parmenter
IntroductionConsumer-based physical activity monitors (PAMs) are becoming increasingly popular, with multiple global organisations recommending physical activity levels that equate to 10,000 steps per day for optimal health. We therefore aimed to compare the step count of five PAMs to a visual step count to identify the most accurate monitors at varying gait speeds, along with the optimal anatomical placement site.MethodsParticipants completed 3min on a treadmill for five speeds (5.0km/h, 6.5km/h, 8.0km/h, 10km/h, 12km/h). An Actigraph wGT3XBT-BT was placed on the waist and wrist, a FitBit One on the waist, and a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on both wrists. A video of participant’s lower limbs was recorded for visual count. Analyses of variance (ANOVAs) were conducted to examine the effects of gait speed and device placement site on step count accuracy.ResultsThirty-one participants (mean age 24.3±5.2yrs) took part. Step count error ranged from 41.3±13.8% for the wrist-worn Actigraph to only 0.04±4.3% and −0.3±4.0% for the waist-worn Fitbit One and Actigraph, respectively. Across all gait speeds, waist-worn devices achieved better accuracy than those on the wrist (p<0.001). The Jawbone was the most accurate wrist-worn consumer-based device at slower speeds (p=0.026), with the Fitbit Flex, and Fitbit Charge HR increasing in accuracy to match the Jawbone at higher speeds.ConclusionThe accuracy and reliability of consumer-based PAMs and the Actigraph is affected by anatomical placement site and walking speed. The Fitbit One and Actigraph on the waist were the strongest performers across all speeds.
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Gait parameter and event estimation using smartphones
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Lucia Pepa, Federica Verdini, Luca Spalazzi
Background and objectivesThe use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well.MethodsThis work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland–Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects.ResultsHigh correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count.ConclusionThis study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
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Source:Gait & Posture, Volume 57
Author(s): Lucia Pepa, Federica Verdini, Luca Spalazzi
Background and objectivesThe use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well.MethodsThis work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland–Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects.ResultsHigh correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count.ConclusionThis study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
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A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Rafael Caldas, Marion Mundt, Wolfgang Potthast, Fernando Buarque de Lima Neto, Bernd Markert
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
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Source:Gait & Posture, Volume 57
Author(s): Rafael Caldas, Marion Mundt, Wolfgang Potthast, Fernando Buarque de Lima Neto, Bernd Markert
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
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Cognitive-motor interference in multiple sclerosis: What happens when the gait speed is fixed?
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Ofir Malcay, Yevgenia Grinberg, Shani Berkowitz, Leora Hershkovitz, Alon Kalron
During the last decade, numerous studies have confirmed a coupling between walking performance and cognition in people with multiple sclerosis (PwMS). Our aim was to provide new insights into a walking-cognitive dual-task (DT) in PwMS. We tested the DT phenomenon by controlling the walking speed using an instrumented treadmill. Thirty PwMS (20 women) with a mean age 40.1 (SD=12.0) participated in the study. Twenty-one healthy subjects served as controls. Each subject completed a sequence of tests: a) Normal walking (ST) − the participant walked on the instrumented treadmill at a comfortable walking speed for 1min; b) Cognitive evaluation (ST) − subjects performed two cognitive tests while seated; c) DT cognitive tests performed while walking on the treadmill at the identical speed performed during normal walking. Outcome measures were spatio-temporal parameters of gait (mean and variability), the Word List Generation Test (WLG) and the Serial-3 Subtraction Test. MS participants significantly decreased their cadence while increasing their stride length during the DT condition compared to the ST condition. Non-significant differences were observed for the WLG and Serial-3 Subtraction Cognitive Tests between the ST condition and the DT condition in both the MS and healthy groups. In terms of gait variability parameters, MS subjects demonstrated a 2 to 3-fold greater gait variability compared to the healthy controls. Non-significant differences in gait variability parameters were observed between the ST and DT conditions in both the MS and control groups. This study provides new insights into the DT phenomenon in the MS population.
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Source:Gait & Posture, Volume 57
Author(s): Ofir Malcay, Yevgenia Grinberg, Shani Berkowitz, Leora Hershkovitz, Alon Kalron
During the last decade, numerous studies have confirmed a coupling between walking performance and cognition in people with multiple sclerosis (PwMS). Our aim was to provide new insights into a walking-cognitive dual-task (DT) in PwMS. We tested the DT phenomenon by controlling the walking speed using an instrumented treadmill. Thirty PwMS (20 women) with a mean age 40.1 (SD=12.0) participated in the study. Twenty-one healthy subjects served as controls. Each subject completed a sequence of tests: a) Normal walking (ST) − the participant walked on the instrumented treadmill at a comfortable walking speed for 1min; b) Cognitive evaluation (ST) − subjects performed two cognitive tests while seated; c) DT cognitive tests performed while walking on the treadmill at the identical speed performed during normal walking. Outcome measures were spatio-temporal parameters of gait (mean and variability), the Word List Generation Test (WLG) and the Serial-3 Subtraction Test. MS participants significantly decreased their cadence while increasing their stride length during the DT condition compared to the ST condition. Non-significant differences were observed for the WLG and Serial-3 Subtraction Cognitive Tests between the ST condition and the DT condition in both the MS and healthy groups. In terms of gait variability parameters, MS subjects demonstrated a 2 to 3-fold greater gait variability compared to the healthy controls. Non-significant differences in gait variability parameters were observed between the ST and DT conditions in both the MS and control groups. This study provides new insights into the DT phenomenon in the MS population.
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Accuracy of step count measured by physical activity monitors: The effect of gait speed and anatomical placement site
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Jessica J. Chow, Jeanette M. Thom, Michael A. Wewege, Rachel E. Ward, Belinda J. Parmenter
IntroductionConsumer-based physical activity monitors (PAMs) are becoming increasingly popular, with multiple global organisations recommending physical activity levels that equate to 10,000 steps per day for optimal health. We therefore aimed to compare the step count of five PAMs to a visual step count to identify the most accurate monitors at varying gait speeds, along with the optimal anatomical placement site.MethodsParticipants completed 3min on a treadmill for five speeds (5.0km/h, 6.5km/h, 8.0km/h, 10km/h, 12km/h). An Actigraph wGT3XBT-BT was placed on the waist and wrist, a FitBit One on the waist, and a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on both wrists. A video of participant’s lower limbs was recorded for visual count. Analyses of variance (ANOVAs) were conducted to examine the effects of gait speed and device placement site on step count accuracy.ResultsThirty-one participants (mean age 24.3±5.2yrs) took part. Step count error ranged from 41.3±13.8% for the wrist-worn Actigraph to only 0.04±4.3% and −0.3±4.0% for the waist-worn Fitbit One and Actigraph, respectively. Across all gait speeds, waist-worn devices achieved better accuracy than those on the wrist (p<0.001). The Jawbone was the most accurate wrist-worn consumer-based device at slower speeds (p=0.026), with the Fitbit Flex, and Fitbit Charge HR increasing in accuracy to match the Jawbone at higher speeds.ConclusionThe accuracy and reliability of consumer-based PAMs and the Actigraph is affected by anatomical placement site and walking speed. The Fitbit One and Actigraph on the waist were the strongest performers across all speeds.
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Source:Gait & Posture, Volume 57
Author(s): Jessica J. Chow, Jeanette M. Thom, Michael A. Wewege, Rachel E. Ward, Belinda J. Parmenter
IntroductionConsumer-based physical activity monitors (PAMs) are becoming increasingly popular, with multiple global organisations recommending physical activity levels that equate to 10,000 steps per day for optimal health. We therefore aimed to compare the step count of five PAMs to a visual step count to identify the most accurate monitors at varying gait speeds, along with the optimal anatomical placement site.MethodsParticipants completed 3min on a treadmill for five speeds (5.0km/h, 6.5km/h, 8.0km/h, 10km/h, 12km/h). An Actigraph wGT3XBT-BT was placed on the waist and wrist, a FitBit One on the waist, and a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on both wrists. A video of participant’s lower limbs was recorded for visual count. Analyses of variance (ANOVAs) were conducted to examine the effects of gait speed and device placement site on step count accuracy.ResultsThirty-one participants (mean age 24.3±5.2yrs) took part. Step count error ranged from 41.3±13.8% for the wrist-worn Actigraph to only 0.04±4.3% and −0.3±4.0% for the waist-worn Fitbit One and Actigraph, respectively. Across all gait speeds, waist-worn devices achieved better accuracy than those on the wrist (p<0.001). The Jawbone was the most accurate wrist-worn consumer-based device at slower speeds (p=0.026), with the Fitbit Flex, and Fitbit Charge HR increasing in accuracy to match the Jawbone at higher speeds.ConclusionThe accuracy and reliability of consumer-based PAMs and the Actigraph is affected by anatomical placement site and walking speed. The Fitbit One and Actigraph on the waist were the strongest performers across all speeds.
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Gait parameter and event estimation using smartphones
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Lucia Pepa, Federica Verdini, Luca Spalazzi
Background and objectivesThe use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well.MethodsThis work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland–Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects.ResultsHigh correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count.ConclusionThis study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
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Source:Gait & Posture, Volume 57
Author(s): Lucia Pepa, Federica Verdini, Luca Spalazzi
Background and objectivesThe use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well.MethodsThis work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland–Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects.ResultsHigh correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count.ConclusionThis study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
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A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Rafael Caldas, Marion Mundt, Wolfgang Potthast, Fernando Buarque de Lima Neto, Bernd Markert
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
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Source:Gait & Posture, Volume 57
Author(s): Rafael Caldas, Marion Mundt, Wolfgang Potthast, Fernando Buarque de Lima Neto, Bernd Markert
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
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Cognitive-motor interference in multiple sclerosis: What happens when the gait speed is fixed?
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Ofir Malcay, Yevgenia Grinberg, Shani Berkowitz, Leora Hershkovitz, Alon Kalron
During the last decade, numerous studies have confirmed a coupling between walking performance and cognition in people with multiple sclerosis (PwMS). Our aim was to provide new insights into a walking-cognitive dual-task (DT) in PwMS. We tested the DT phenomenon by controlling the walking speed using an instrumented treadmill. Thirty PwMS (20 women) with a mean age 40.1 (SD=12.0) participated in the study. Twenty-one healthy subjects served as controls. Each subject completed a sequence of tests: a) Normal walking (ST) − the participant walked on the instrumented treadmill at a comfortable walking speed for 1min; b) Cognitive evaluation (ST) − subjects performed two cognitive tests while seated; c) DT cognitive tests performed while walking on the treadmill at the identical speed performed during normal walking. Outcome measures were spatio-temporal parameters of gait (mean and variability), the Word List Generation Test (WLG) and the Serial-3 Subtraction Test. MS participants significantly decreased their cadence while increasing their stride length during the DT condition compared to the ST condition. Non-significant differences were observed for the WLG and Serial-3 Subtraction Cognitive Tests between the ST condition and the DT condition in both the MS and healthy groups. In terms of gait variability parameters, MS subjects demonstrated a 2 to 3-fold greater gait variability compared to the healthy controls. Non-significant differences in gait variability parameters were observed between the ST and DT conditions in both the MS and control groups. This study provides new insights into the DT phenomenon in the MS population.
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Source:Gait & Posture, Volume 57
Author(s): Ofir Malcay, Yevgenia Grinberg, Shani Berkowitz, Leora Hershkovitz, Alon Kalron
During the last decade, numerous studies have confirmed a coupling between walking performance and cognition in people with multiple sclerosis (PwMS). Our aim was to provide new insights into a walking-cognitive dual-task (DT) in PwMS. We tested the DT phenomenon by controlling the walking speed using an instrumented treadmill. Thirty PwMS (20 women) with a mean age 40.1 (SD=12.0) participated in the study. Twenty-one healthy subjects served as controls. Each subject completed a sequence of tests: a) Normal walking (ST) − the participant walked on the instrumented treadmill at a comfortable walking speed for 1min; b) Cognitive evaluation (ST) − subjects performed two cognitive tests while seated; c) DT cognitive tests performed while walking on the treadmill at the identical speed performed during normal walking. Outcome measures were spatio-temporal parameters of gait (mean and variability), the Word List Generation Test (WLG) and the Serial-3 Subtraction Test. MS participants significantly decreased their cadence while increasing their stride length during the DT condition compared to the ST condition. Non-significant differences were observed for the WLG and Serial-3 Subtraction Cognitive Tests between the ST condition and the DT condition in both the MS and healthy groups. In terms of gait variability parameters, MS subjects demonstrated a 2 to 3-fold greater gait variability compared to the healthy controls. Non-significant differences in gait variability parameters were observed between the ST and DT conditions in both the MS and control groups. This study provides new insights into the DT phenomenon in the MS population.
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Accuracy of step count measured by physical activity monitors: The effect of gait speed and anatomical placement site
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Jessica J. Chow, Jeanette M. Thom, Michael A. Wewege, Rachel E. Ward, Belinda J. Parmenter
IntroductionConsumer-based physical activity monitors (PAMs) are becoming increasingly popular, with multiple global organisations recommending physical activity levels that equate to 10,000 steps per day for optimal health. We therefore aimed to compare the step count of five PAMs to a visual step count to identify the most accurate monitors at varying gait speeds, along with the optimal anatomical placement site.MethodsParticipants completed 3min on a treadmill for five speeds (5.0km/h, 6.5km/h, 8.0km/h, 10km/h, 12km/h). An Actigraph wGT3XBT-BT was placed on the waist and wrist, a FitBit One on the waist, and a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on both wrists. A video of participant’s lower limbs was recorded for visual count. Analyses of variance (ANOVAs) were conducted to examine the effects of gait speed and device placement site on step count accuracy.ResultsThirty-one participants (mean age 24.3±5.2yrs) took part. Step count error ranged from 41.3±13.8% for the wrist-worn Actigraph to only 0.04±4.3% and −0.3±4.0% for the waist-worn Fitbit One and Actigraph, respectively. Across all gait speeds, waist-worn devices achieved better accuracy than those on the wrist (p<0.001). The Jawbone was the most accurate wrist-worn consumer-based device at slower speeds (p=0.026), with the Fitbit Flex, and Fitbit Charge HR increasing in accuracy to match the Jawbone at higher speeds.ConclusionThe accuracy and reliability of consumer-based PAMs and the Actigraph is affected by anatomical placement site and walking speed. The Fitbit One and Actigraph on the waist were the strongest performers across all speeds.
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Source:Gait & Posture, Volume 57
Author(s): Jessica J. Chow, Jeanette M. Thom, Michael A. Wewege, Rachel E. Ward, Belinda J. Parmenter
IntroductionConsumer-based physical activity monitors (PAMs) are becoming increasingly popular, with multiple global organisations recommending physical activity levels that equate to 10,000 steps per day for optimal health. We therefore aimed to compare the step count of five PAMs to a visual step count to identify the most accurate monitors at varying gait speeds, along with the optimal anatomical placement site.MethodsParticipants completed 3min on a treadmill for five speeds (5.0km/h, 6.5km/h, 8.0km/h, 10km/h, 12km/h). An Actigraph wGT3XBT-BT was placed on the waist and wrist, a FitBit One on the waist, and a Fitbit Flex, Fitbit Charge HR and Jawbone UP24 on both wrists. A video of participant’s lower limbs was recorded for visual count. Analyses of variance (ANOVAs) were conducted to examine the effects of gait speed and device placement site on step count accuracy.ResultsThirty-one participants (mean age 24.3±5.2yrs) took part. Step count error ranged from 41.3±13.8% for the wrist-worn Actigraph to only 0.04±4.3% and −0.3±4.0% for the waist-worn Fitbit One and Actigraph, respectively. Across all gait speeds, waist-worn devices achieved better accuracy than those on the wrist (p<0.001). The Jawbone was the most accurate wrist-worn consumer-based device at slower speeds (p=0.026), with the Fitbit Flex, and Fitbit Charge HR increasing in accuracy to match the Jawbone at higher speeds.ConclusionThe accuracy and reliability of consumer-based PAMs and the Actigraph is affected by anatomical placement site and walking speed. The Fitbit One and Actigraph on the waist were the strongest performers across all speeds.
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Gait parameter and event estimation using smartphones
Publication date: September 2017
Source:Gait & Posture, Volume 57
Author(s): Lucia Pepa, Federica Verdini, Luca Spalazzi
Background and objectivesThe use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well.MethodsThis work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland–Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects.ResultsHigh correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count.ConclusionThis study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
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Source:Gait & Posture, Volume 57
Author(s): Lucia Pepa, Federica Verdini, Luca Spalazzi
Background and objectivesThe use of smartphones can greatly help for gait parameters estimation during daily living, but its accuracy needs a deeper evaluation against a gold standard. The objective of the paper is a step-by-step assessment of smartphone performance in heel strike, step count, step period, and step length estimation. The influence of smartphone placement and orientation on estimation performance is evaluated as well.MethodsThis work relies on a smartphone app developed to acquire, process, and store inertial sensor data and rotation matrices about device position. Smartphone alignment was evaluated by expressing the acceleration vector in three reference frames. Two smartphone placements were tested. Three methods for heel strike detection were considered. On the basis of estimated heel strikes, step count is performed, step period is obtained, and the inverted pendulum model is applied for step length estimation. Pearson correlation coefficient, absolute and relative errors, ANOVA, and Bland–Altman limits of agreement were used to compare smartphone estimation with stereophotogrammetry on eleven healthy subjects.ResultsHigh correlations were found between smartphone and stereophotogrammetric measures: up to 0.93 for step count, to 0.99 for heel strike, 0.96 for step period, and 0.92 for step length. Error ranges are comparable to those in the literature. Smartphone placement did not affect the performance. The major influence of acceleration reference frames and heel strike detection method was found in step count.ConclusionThis study provides detailed information about expected accuracy when smartphone is used as a gait monitoring tool. The obtained results encourage real life applications.
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Porównanie częstości występowania objawów współistniejących w grupie pacjentów z zawrotami głowy.
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Related Articles |
Porównanie częstości występowania objawów współistniejących w grupie pacjentów z zawrotami głowy.
Otolaryngol Pol. 2017 Feb 28;71(1):34-39
Authors: Šichnárek J, Vyskotová J, Macháčková K, Mrázková E
Abstract
BACKGROUND: Dizziness is the second most frequent symptom that make patients seek specialized examination. The effective solution of dizzy conditions requires treatment in cooperation with different branches of medicine.
OBJECTIVE: To analyze data from the database of patients with vertigo examined in the Hearing and Balance Disorder Centre in Ostrava, to find out whether, and in what factors, the sets of patients with diagnosed central and peripheral vestibular syndrom differ from each other.
METHODS: Retrospective study that was carried out from October 2012 to February 2013. The data was gathered from documentation of all vertiginous patients who were examined by an otoneurologist.
RESULTS: The statistically significant difference between the two sets was found in: occurrence of hypertension and mild obesity, impaired hearing and otitis media, stabilometric testing CONCLUSIONS: There was a statistically significant difference between the sets with the central and peripheral vestibular syndrome in the frequency of occurrence of hypertension, impaired hearing, otitis media, in mild obesity categorization and in balance disorders. There was not any statistically significant difference found in the other observed factors. The results confirm the need of a multidisciplinary approach to patients with vertigo.
PMID: 28485291 [PubMed - indexed for MEDLINE]
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Quantitative Pneumatic Otoscopy Using a Light-Based Ranging Technique
Abstract
Otitis media is the leading cause of hearing loss in children. It is commonly associated with fluid in the ear, which can result in up to 45 dB of hearing loss for extended periods of time during a child’s most important developmental years. Accurate assessment of middle ear effusions is an important part of understanding otitis media. Current technologies used to diagnose otitis media with effusion are pneumatic otoscopy, tympanometry, and acoustic reflectometry. While all of these techniques can reasonably diagnose the presence of an effusion, they provide limited information about the infection present behind the tympanic membrane.
We have developed a technique based on low-coherence interferometry—a non-invasive optical ranging technique capable of sensing depth-resolved microscopic scattering features through the eardrum—to quantify eardrum thickness and integrity, as well as detect any effusion, purulence, or biofilm behind the tympanic membrane. In this manuscript, the technique is coupled with a pneumatic otoscope to measure minute deflections of the tympanic membrane from insufflation pressure stimuli. This results in quantitative measurements of tympanic membrane mobility, which may be used to gain a better understanding of the impact of infection on the membrane dynamics. A small pilot study of 15 subjects demonstrates the ability of pneumatic low-coherence interferometry to quantitatively differentiate normal ears from ears with effusions present. Analysis of the strengths and weaknesses of the technique, as well as focus areas of future research, is also discussed.
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