Measuring early pre-symptomatic changes in locomotion of SOD1-G93A rats—A rodent model of amyotrophic lateral sclerosis

Tang, Wenlong; Tasch, U.; Neerchal, Nagaraj K.; Zhu, Liang; Yarowsky, Paul

doi:10.1016/j.jneumeth.2008.08.032

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…The proposed method is not a diagnostic tool like the one presented in [30,31]; rather it is an assessment or evaluation model. It provides valuable information regarding the relative impairment level compared to healthy control group.…”

Section: Muscle Activitymentioning

confidence: 99%

Characterization and quantification of gait deficits within gait phases using fuzzy-granular computing

Bogale¹,

Yu²,

Sarkodie-Gyan³

et al. 2012

JBiSE

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People with neurological disorders like Cerebral Palsy (CP) and Multiple Sclerosis (MS) suffer associated functional gait problems. The symptoms and sign of these gait deficits are different between subjects and even within a subject at different stage of the disease. Identifying these gait related abnormalities helps in the treatment planning and rehabilitation process. The current gait assessment process does not provide very specific information within the seven gait phases. The objective of this study is to investigate the possible application of granular computing to quantify gait parameters within the seven gait phases. In this process we applied fuzzy-granular computing on the vertical ground reaction force (VGRF) and surface electromyography (sEMG) data to obtain respective characteristic values for each gait phase. A fuzzy similarity (FS) measure is used to compare patient values with age and sex matched control able-bodied group. We specifically applied and tested this approach on 10 patients (4 Cerebral Palsy and 6 Multiple Sclerosis) to identify possible gait abnormalities. Different FS values for VGRF for right and left leg is observed. The VGRF analysis shows smaller FS values during the swing phase in CP and MS subjects that are evidence of associated stability problem. Similarly, FS values for muscle activates of the four-selected muscle display a broad range of values due to difference between subjects. Degraded FS values for different muscles at different stage of the gait cycle are reported. Smaller FS values are sign of abnormal activity of the respective muscles. This approach provides individual centered and very specific information within the gait phases that can be employed for diagnosis, treatment and rehabilitation process

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Section: Muscle Activitymentioning

confidence: 99%

Characterization and quantification of gait deficits within gait phases using fuzzy-granular computing

Bogale¹,

Yu²,

Sarkodie-Gyan³

et al. 2012

JBiSE

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“…It is reported that the kids with autism have weak muscles and may result fewer daily activities than healthy kids [5]. The same situation exists in the post stoke patients [6] and patients with Amyotrophic Lateral Sclerosis [7]. Thus, monitoring physical daily activities can assist clinical diagnosis of the diseases, and most importantly, can help people to have a healthy life style.…”

Section: Introductionmentioning

confidence: 99%

Highly accurate classification of postures and activities by a shoe-based monitor through classification with rejection

Tang

Sazonov

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Monitoring human beings' major daily activities is important for many biomedical studies. Some monitoring applications may require highly reliable identification of certain postures and activities with desired accuracies well above 99% mark. This paper suggests a method for performing highly accurate classification of postures and activities from data collected by a wearable shoe monitor (SmartShoe) through classification with rejection. The classifier used in this study is support vector machines that uses posterior probability based on the distance of an observation to the separating hyperplane to reject unreliable observations. The results show that a significant improvement (from 95.2% ± 3.5% to 99% ± 1%) of the classification accuracy has been reached after the rejection, as compared to the accuracy reported previously. Such an approach will be especially beneficial in application where high accuracy of recognition is desired while not all observations need to be assigned a class label.

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“…The gait analysis system (GAS™) (Tasch et al, 2008) is capable of measuring 20 different parameters of locomotion and provides a non-invasive measure of locomotion in individual rats over time. We recently used it to distinguish differences in gait between control and SOD1-G93A rats, a model of amyotrophic lateral sclerosis in man, at pre-symptomatic stages (Tang et al, 2009). Here we test the hypothesis that our quantitative measurements can detect changes in gait that are induced by permanent impairment of function of a single lower hindlimb, and further, that it can detect subtle changes in gait, linked to the partial and temporary loss of muscle function.…”

Section: Introductionmentioning

confidence: 99%

Gait analysis of locomotory impairment in rats before and after neuromuscular injury

Tang

Lovering

Roche

et al. 2009

Journal of Neuroscience Methods

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We used a gait analysis system (GAS™) to measure the changes in locomotion parameters of adult Sprague–Dawley rats after neuromuscular injury, induced by repeated large-strain lengthening contractions of the dorsiflexors muscles. We developed a logistic regression model from test runs of control and permanently impaired (denervation of the dorsiflexor muscles) rats and used this model to predict the probabilities of locomotory impairment in rats injured by lengthening contractions. The data showed that GAS™ predicts the probability of locomotory impairment with very high reliability, with values close to 100% immediately after injury and close to 0% after severalweeks of recovery from injury. The six transformed locomotion parameters most effective in the model were in three domains: frequency, force, and time. We conclude that application of the GAS™ instrument with our predictive model accurately identifies locomotory changes due to neuromuscular deficits. Use of this technology should be valuable for monitoring the progression of a neuromuscular disease and the effects of therapeutic interventions.

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Measuring early pre-symptomatic changes in locomotion of SOD1-G93A rats—A rodent model of amyotrophic lateral sclerosis

Cited by 14 publications

References 9 publications

Characterization and quantification of gait deficits within gait phases using fuzzy-granular computing

Characterization and quantification of gait deficits within gait phases using fuzzy-granular computing

Highly accurate classification of postures and activities by a shoe-based monitor through classification with rejection

Gait analysis of locomotory impairment in rats before and after neuromuscular injury

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