The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Timothy Mastroianni - One of the best experts on this subject based on the ideXlab platform.
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implementation of a smartphone wireless gyroscope platform with machine learning for classifying disparity of a hemiplegic patellar Tendon Reflex pair
Journal of Mechanics in Medicine and Biology, 2017Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:The patellar Tendon Reflex response provides fundamental means of assessing a subject’s neurological health. Dysfunction regarding the characteristics of the Reflex response may warrant the escalation to more advanced diagnostic techniques. Current strategies involve the manual elicitation of the patellar Tendon Reflex by a highly skilled clinician with subsequent interpretation according to an ordinal scale. The reliability of the ordinal scale approach is a topic of contention. Highly skilled clinicians have been in disagreement regarding even the observation of asymmetric Reflex pairs. An alternative strategy incorporated the ubiquitous smartphone with a software application to function as a wireless gyroscope platform for quantifying the Reflex response. Each gyroscope signal recording of the Reflex response can be conveyed wirelessly through Internet connectivity as an email attachment. The Reflex response is evoked through a potential energy impact pendulum that enables prescribed targeting and potential energy level. The smartphone functioning as a wireless gyroscope platform reveals an observationally representative gyroscope signal of the Reflex response. Three notably distinguishable attributes of the Reflex response are incorporated into a feature set for machine learning: maximum angular rate of rotation, minimum angular rate of rotation, and time disparity between maximum and minimum angular rate of rotation. Four machine learning platforms such as the J48 decision tree, K-nearest neighbors, logistic regression, and support vector machine, were applied to the patellar Tendon Reflex response feature set incorporating a hemiplegic patellar Tendon Reflex pair. The J48 decision tree attained 98% classification accuracy, and the K-nearest neighbors, logistic regression, and support vector machine achieved perfect classification accuracy for distinguishing between a hemiplegic affected leg and unaffected leg patellar Tendon Reflex pair. The research findings reveal the potential of machine learning for enabling advanced diagnostic acuity respective of the gyroscope signal of the patellar Tendon Reflex response.
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Smartphone wireless gyroscope platform for machine learning classification of hemiplegic patellar Tendon Reflex pair disparity through a multilayer perceptron neural network
2016 IEEE Wireless Health (WH), 2016Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:The patellar Tendon enables fundamental insight regarding neurological health status. Clinically observed dysfunction may warrant escalation to more advanced and expensive medical diagnostics. Conventionally clinicians apply an ordinal scale to quantify Reflex response characteristics. However the reliability of ordinal scales is a subject of debate, and even highly skilled clinicians have disputed the observation of an asymmetric Reflex pair. An alternative is the use of the wireless quantified Reflex system, which features an impact pendulum attached to a Reflex hammer for providing precisely targeted levels of potential energy with a smartphone (iPhone) equipped with software to function as a wireless gyroscope platform that can email a trial sample as an email attachment by wireless connectivity to the Internet. With notable attributes of the gyroscope signal recordings of the Reflex response of a hemiplegic patellar Tendon Reflex pair observed a feature set is developed for machine learning classification. Using the multilayer perceptron neural network considerable classification accuracy is attained. The research implications reveal the potential of integrating machine learning with a wireless Reflex quantification system that applies a smartphone (iPhone) as a wireless gyroscope platform.
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use of smartphones and portable media devices for quantifying human movement characteristics of gait Tendon Reflex response and parkinson s disease hand tremor
Methods of Molecular Biology, 2015Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:Smartphones and portable media devices are both equipped with sensor components, such as accelerometers. A software application enables these devices to function as a robust wireless accelerometer platform. The recorded accelerometer waveform can be transmitted wireless as an e-mail attachment through connectivity to the Internet. The implication of such devices as a wireless accelerometer platform is the experimental and post-processing locations can be placed anywhere in the world. Gait was quantified by mounting a smartphone or portable media device proximal to the lateral malleolus of the ankle joint. Attributes of the gait cycle were quantified with a considerable accuracy and reliability. The patellar Tendon Reflex response was quantified by using the device in tandem with a potential energy impact pendulum to evoke the patellar Tendon Reflex. The acceleration waveform maximum acceleration feature of the Reflex response displayed considerable accuracy and reliability. By mounting the smartphone or portable media device to the dorsum of the hand through a glove, Parkinson's disease hand tremor was quantified and contrasted with significance to a non-Parkinson's disease steady hand control. With the methods advocated in this chapter, any aspect of human movement may be quantified through smartphones or portable media devices and post-processed anywhere in the world. These wearable devices are anticipated to substantially impact the biomedical and healthcare industry. Language: en
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use of smartphones and portable media devices for quantifying human movement characteristics of gait Tendon Reflex response and parkinson s disease hand tremor
Methods of Molecular Biology, 2015Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:Smartphones and portable media devices are both equipped with sensor components, such as accelerometers. A software application enables these devices to function as a robust wireless accelerometer platform. The recorded accelerometer waveform can be transmitted wireless as an e-mail attachment through connectivity to the Internet. The implication of such devices as a wireless accelerometer platform is the experimental and post-processing locations can be placed anywhere in the world. Gait was quantified by mounting a smartphone or portable media device proximal to the lateral malleolus of the ankle joint. Attributes of the gait cycle were quantified with a considerable accuracy and reliability. The patellar Tendon Reflex response was quantified by using the device in tandem with a potential energy impact pendulum to evoke the patellar Tendon Reflex. The acceleration waveform maximum acceleration feature of the Reflex response displayed considerable accuracy and reliability. By mounting the smartphone or portable media device to the dorsum of the hand through a glove, Parkinson's disease hand tremor was quantified and contrasted with significance to a non-Parkinson's disease steady hand control. With the methods advocated in this chapter, any aspect of human movement may be quantified through smartphones or portable media devices and post-processed anywhere in the world. These wearable devices are anticipated to substantially impact the biomedical and healthcare industry.
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implementation of an ipod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar Tendon Reflex pair
Journal of Medical Imaging and Health Informatics, 2014Co-Authors: Robert Lemoyne, Wesley T Kerr, Kevin Zanjani, Timothy MastroianniAbstract:The characteristics of the patellar Tendon Reflex provide fundamental insight regarding the diagnosis of neurological status. Based on the features of the Tendon Reflex response, a clinician may establish preliminary perspective regarding the global condition of the nervous system. Current techniques for quantifying the observations of the Reflex response involve the application of ordinal scales, requiring the expertise of a highly skilled clinician. However, the reliability of the ordinal scale approach is debatable. Highly skilled clinicians have even disputed the presence of asymmetric Reflex pairs. An alternative strategy was the implementation of an iPod wireless accelerometer application to quantify the Reflex response acceleration waveform. An application enabled the recording of the acceleration waveform and later wireless transmission as an email attachment by connectivity to the Internet. A potential energy impact pendulum enabled the patellar Tendon Reflex to be evoked in a predetermined and targeted manner. Three feature categories of the Reflex response acceleration waveform (global parameters, temporal organization, and spectral features) were incorporated into machine learning to distinguish a subject's hemiplegic and healthy Reflex pair. Machine learning attained perfect classification of the hemiplegic and healthy Reflex pair. The research findings implicate the promise of machine learning for providing increased diagnostic acuity regarding the acceleration waveform of the Tendon Reflex response.
Robert Lemoyne - One of the best experts on this subject based on the ideXlab platform.
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implementation of a smartphone wireless gyroscope platform with machine learning for classifying disparity of a hemiplegic patellar Tendon Reflex pair
Journal of Mechanics in Medicine and Biology, 2017Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:The patellar Tendon Reflex response provides fundamental means of assessing a subject’s neurological health. Dysfunction regarding the characteristics of the Reflex response may warrant the escalation to more advanced diagnostic techniques. Current strategies involve the manual elicitation of the patellar Tendon Reflex by a highly skilled clinician with subsequent interpretation according to an ordinal scale. The reliability of the ordinal scale approach is a topic of contention. Highly skilled clinicians have been in disagreement regarding even the observation of asymmetric Reflex pairs. An alternative strategy incorporated the ubiquitous smartphone with a software application to function as a wireless gyroscope platform for quantifying the Reflex response. Each gyroscope signal recording of the Reflex response can be conveyed wirelessly through Internet connectivity as an email attachment. The Reflex response is evoked through a potential energy impact pendulum that enables prescribed targeting and potential energy level. The smartphone functioning as a wireless gyroscope platform reveals an observationally representative gyroscope signal of the Reflex response. Three notably distinguishable attributes of the Reflex response are incorporated into a feature set for machine learning: maximum angular rate of rotation, minimum angular rate of rotation, and time disparity between maximum and minimum angular rate of rotation. Four machine learning platforms such as the J48 decision tree, K-nearest neighbors, logistic regression, and support vector machine, were applied to the patellar Tendon Reflex response feature set incorporating a hemiplegic patellar Tendon Reflex pair. The J48 decision tree attained 98% classification accuracy, and the K-nearest neighbors, logistic regression, and support vector machine achieved perfect classification accuracy for distinguishing between a hemiplegic affected leg and unaffected leg patellar Tendon Reflex pair. The research findings reveal the potential of machine learning for enabling advanced diagnostic acuity respective of the gyroscope signal of the patellar Tendon Reflex response.
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Smartphone wireless gyroscope platform for machine learning classification of hemiplegic patellar Tendon Reflex pair disparity through a multilayer perceptron neural network
2016 IEEE Wireless Health (WH), 2016Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:The patellar Tendon enables fundamental insight regarding neurological health status. Clinically observed dysfunction may warrant escalation to more advanced and expensive medical diagnostics. Conventionally clinicians apply an ordinal scale to quantify Reflex response characteristics. However the reliability of ordinal scales is a subject of debate, and even highly skilled clinicians have disputed the observation of an asymmetric Reflex pair. An alternative is the use of the wireless quantified Reflex system, which features an impact pendulum attached to a Reflex hammer for providing precisely targeted levels of potential energy with a smartphone (iPhone) equipped with software to function as a wireless gyroscope platform that can email a trial sample as an email attachment by wireless connectivity to the Internet. With notable attributes of the gyroscope signal recordings of the Reflex response of a hemiplegic patellar Tendon Reflex pair observed a feature set is developed for machine learning classification. Using the multilayer perceptron neural network considerable classification accuracy is attained. The research implications reveal the potential of integrating machine learning with a wireless Reflex quantification system that applies a smartphone (iPhone) as a wireless gyroscope platform.
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use of smartphones and portable media devices for quantifying human movement characteristics of gait Tendon Reflex response and parkinson s disease hand tremor
Methods of Molecular Biology, 2015Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:Smartphones and portable media devices are both equipped with sensor components, such as accelerometers. A software application enables these devices to function as a robust wireless accelerometer platform. The recorded accelerometer waveform can be transmitted wireless as an e-mail attachment through connectivity to the Internet. The implication of such devices as a wireless accelerometer platform is the experimental and post-processing locations can be placed anywhere in the world. Gait was quantified by mounting a smartphone or portable media device proximal to the lateral malleolus of the ankle joint. Attributes of the gait cycle were quantified with a considerable accuracy and reliability. The patellar Tendon Reflex response was quantified by using the device in tandem with a potential energy impact pendulum to evoke the patellar Tendon Reflex. The acceleration waveform maximum acceleration feature of the Reflex response displayed considerable accuracy and reliability. By mounting the smartphone or portable media device to the dorsum of the hand through a glove, Parkinson's disease hand tremor was quantified and contrasted with significance to a non-Parkinson's disease steady hand control. With the methods advocated in this chapter, any aspect of human movement may be quantified through smartphones or portable media devices and post-processed anywhere in the world. These wearable devices are anticipated to substantially impact the biomedical and healthcare industry. Language: en
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use of smartphones and portable media devices for quantifying human movement characteristics of gait Tendon Reflex response and parkinson s disease hand tremor
Methods of Molecular Biology, 2015Co-Authors: Robert Lemoyne, Timothy MastroianniAbstract:Smartphones and portable media devices are both equipped with sensor components, such as accelerometers. A software application enables these devices to function as a robust wireless accelerometer platform. The recorded accelerometer waveform can be transmitted wireless as an e-mail attachment through connectivity to the Internet. The implication of such devices as a wireless accelerometer platform is the experimental and post-processing locations can be placed anywhere in the world. Gait was quantified by mounting a smartphone or portable media device proximal to the lateral malleolus of the ankle joint. Attributes of the gait cycle were quantified with a considerable accuracy and reliability. The patellar Tendon Reflex response was quantified by using the device in tandem with a potential energy impact pendulum to evoke the patellar Tendon Reflex. The acceleration waveform maximum acceleration feature of the Reflex response displayed considerable accuracy and reliability. By mounting the smartphone or portable media device to the dorsum of the hand through a glove, Parkinson's disease hand tremor was quantified and contrasted with significance to a non-Parkinson's disease steady hand control. With the methods advocated in this chapter, any aspect of human movement may be quantified through smartphones or portable media devices and post-processed anywhere in the world. These wearable devices are anticipated to substantially impact the biomedical and healthcare industry.
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implementation of an ipod wireless accelerometer application using machine learning to classify disparity of hemiplegic and healthy patellar Tendon Reflex pair
Journal of Medical Imaging and Health Informatics, 2014Co-Authors: Robert Lemoyne, Wesley T Kerr, Kevin Zanjani, Timothy MastroianniAbstract:The characteristics of the patellar Tendon Reflex provide fundamental insight regarding the diagnosis of neurological status. Based on the features of the Tendon Reflex response, a clinician may establish preliminary perspective regarding the global condition of the nervous system. Current techniques for quantifying the observations of the Reflex response involve the application of ordinal scales, requiring the expertise of a highly skilled clinician. However, the reliability of the ordinal scale approach is debatable. Highly skilled clinicians have even disputed the presence of asymmetric Reflex pairs. An alternative strategy was the implementation of an iPod wireless accelerometer application to quantify the Reflex response acceleration waveform. An application enabled the recording of the acceleration waveform and later wireless transmission as an email attachment by connectivity to the Internet. A potential energy impact pendulum enabled the patellar Tendon Reflex to be evoked in a predetermined and targeted manner. Three feature categories of the Reflex response acceleration waveform (global parameters, temporal organization, and spectral features) were incorporated into machine learning to distinguish a subject's hemiplegic and healthy Reflex pair. Machine learning attained perfect classification of the hemiplegic and healthy Reflex pair. The research findings implicate the promise of machine learning for providing increased diagnostic acuity regarding the acceleration waveform of the Tendon Reflex response.
Jose Berciano - One of the best experts on this subject based on the ideXlab platform.
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reversible conduction failure on the deep Tendon Reflex response recording in early guillain barre syndrome
Clinical Neurophysiology Practice, 2018Co-Authors: Antonio G Garcia, Silvia Alvarezparadelo, Maria J Sedano, Jose BercianoAbstract:Abstract Objective To describe the case of a patient with Guillain-Barre syndrome (GBS) showing early reversible conduction failure (RCF) detected by means of serial deep Tendon Reflex response (T-Reflex) study. Methods A 36-year-old woman had a 5-day history of foot and hand paresthesias ascending to thighs and arms, throbbing interscapular and neck pain, mild to moderate tetraparesis, and aReflexia. Nerve conduction studies (NCS) were performed on days 7 and 33 after onset. Results NCS showed an equivocal electrophysiologic pattern, just an isolated distal RCF being detected on the right radial nerve at initial examination. Motor latency on deltoid muscle after Erb’s point stimulation was preserved. Sensory conduction velocities were normal or slightly slowed. Somatosensory evoked potentials from median and tibial nerves were normal. Initially, F-wave study demonstrated reversible abnormalities, consisting of multiple A waves and low F-wave persistence, minimal F-wave latencies being preserved. Biceps brachii T-Reflex was normal, whereas Achilles T-Reflex was absent bilaterally, appearing on the second study with normal T-wave morphology and latency, thus conforming to the requirements for RCF diagnosis. Soleus H-Reflex was also initially absent. Conclusions Serial T-Reflex study is a useful technique for detecting early RCF of proximal nerve trunks in early GBS. Significance T-Reflex is useful tool for GBS in association with NCS.
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electromyographic Tendon Reflex recording an accurate and comfortable method for diagnosis of charcot marie tooth disease type 1a
Muscle & Nerve, 2015Co-Authors: Antonio G Garcia, Ana L Pelayonegro, Silvia Alvarezparadelo, Francisco M Antolin, Jose BercianoAbstract:Introduction: We analyzed the utility of Tendon Reflex (T-Reflex) testing in Charcot-Marie-Tooth disease type 1A (CMT1A). Methods: A total of 82 subjects from 27 unrelated CMT1A pedigrees were evaluated prospectively. The series also comprised 28 adult healthy controls. Electrophysiology included evaluation of biceps T-Reflex and soleus T-Reflex. Results: Seventy-one individuals (62 adults and 9 children) had clinical and electrophysiological features of CMT1A. The remaining 11 (8 adults and 3 children) were unaffected. On electrophysiological testing, the biceps T-Reflex was elicited in 58 of 62 (93%) adult CMT1A patients and in all 9 affected children. Latencies of the biceps T-Reflex were always markedly prolonged, and a cut-off limit of 16.25 ms clearly separated adult patients and controls or unaffected kin adult individuals. In affected children, the soleus T-Reflex latency was also prolonged when compared with age and height normative data. Conclusion: T-Reflex testing is an accurate diagnostic technique for CMT1A patients. Muscle Nerve 52: 39–44, 2015
Rüdiger Köhling - One of the best experts on this subject based on the ideXlab platform.
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Repetitive Peripheral Magnetic Nerve Stimulation (rPMS) as Adjuvant Therapy Reduces Skeletal Muscle Reflex Activity.
Frontiers in neurology, 2019Co-Authors: Volker Zschorlich, Martin Hillebrecht, Tammam Tanjour, Frank Behrendt, Timo Kirschstein, Rüdiger KöhlingAbstract:Background: The reduction of muscle hypertonia and spasticity, as well as an increase in mobility, is an essential prerequisite for the amelioration of physiotherapeutical treatments. Repetitive peripheral magnetic nerve stimulation (rPMS) is a putative adjuvant therapy that improves the mobility of patients. Methods: Thirty-eight participants underwent either an rPMS treatment (N=19) with a 5 Hz stimulation protocol at the soleus muscle or with sham stimulation (N=19). The stimulation took place over 5 minutes. The study was conducted in a pre-test post-test design with matched groups. Results: The primary outcome was a significant reduction of the Reflex activity of the soleus muscle, triggered by a computer-aided Tendon-Reflex impact. Outcome measures were taken at the baseline and after the following intervention. The pre-post differences of the Tendon Reflex response activity were -23.7% (P < 0.001) for the treatment group. No significant effects showed in the sham stimulation group. Conclusion: Low-frequency magnetic stimulation (5 Hz rPMS) exhibits a substantial reduction of the Tendon Reflex amplitude. The 5 Hz rPMS treatment seems to be an effective procedure to reduce muscular stiffness, increase mobility, and thus, makes the therapeutic effect of neuro-rehabilitation more effective. For this reason, the 5 Hz rPMS treatment might have the potential to be used as an adjuvant therapy in the rehabilitation of gait and posture control in patients suffering from limited mobility due to spasticity.
Antonio G Garcia - One of the best experts on this subject based on the ideXlab platform.
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reversible conduction failure on the deep Tendon Reflex response recording in early guillain barre syndrome
Clinical Neurophysiology Practice, 2018Co-Authors: Antonio G Garcia, Silvia Alvarezparadelo, Maria J Sedano, Jose BercianoAbstract:Abstract Objective To describe the case of a patient with Guillain-Barre syndrome (GBS) showing early reversible conduction failure (RCF) detected by means of serial deep Tendon Reflex response (T-Reflex) study. Methods A 36-year-old woman had a 5-day history of foot and hand paresthesias ascending to thighs and arms, throbbing interscapular and neck pain, mild to moderate tetraparesis, and aReflexia. Nerve conduction studies (NCS) were performed on days 7 and 33 after onset. Results NCS showed an equivocal electrophysiologic pattern, just an isolated distal RCF being detected on the right radial nerve at initial examination. Motor latency on deltoid muscle after Erb’s point stimulation was preserved. Sensory conduction velocities were normal or slightly slowed. Somatosensory evoked potentials from median and tibial nerves were normal. Initially, F-wave study demonstrated reversible abnormalities, consisting of multiple A waves and low F-wave persistence, minimal F-wave latencies being preserved. Biceps brachii T-Reflex was normal, whereas Achilles T-Reflex was absent bilaterally, appearing on the second study with normal T-wave morphology and latency, thus conforming to the requirements for RCF diagnosis. Soleus H-Reflex was also initially absent. Conclusions Serial T-Reflex study is a useful technique for detecting early RCF of proximal nerve trunks in early GBS. Significance T-Reflex is useful tool for GBS in association with NCS.
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electromyographic Tendon Reflex recording an accurate and comfortable method for diagnosis of charcot marie tooth disease type 1a
Muscle & Nerve, 2015Co-Authors: Antonio G Garcia, Ana L Pelayonegro, Silvia Alvarezparadelo, Francisco M Antolin, Jose BercianoAbstract:Introduction: We analyzed the utility of Tendon Reflex (T-Reflex) testing in Charcot-Marie-Tooth disease type 1A (CMT1A). Methods: A total of 82 subjects from 27 unrelated CMT1A pedigrees were evaluated prospectively. The series also comprised 28 adult healthy controls. Electrophysiology included evaluation of biceps T-Reflex and soleus T-Reflex. Results: Seventy-one individuals (62 adults and 9 children) had clinical and electrophysiological features of CMT1A. The remaining 11 (8 adults and 3 children) were unaffected. On electrophysiological testing, the biceps T-Reflex was elicited in 58 of 62 (93%) adult CMT1A patients and in all 9 affected children. Latencies of the biceps T-Reflex were always markedly prolonged, and a cut-off limit of 16.25 ms clearly separated adult patients and controls or unaffected kin adult individuals. In affected children, the soleus T-Reflex latency was also prolonged when compared with age and height normative data. Conclusion: T-Reflex testing is an accurate diagnostic technique for CMT1A patients. Muscle Nerve 52: 39–44, 2015
