The availability of an objective clinical evaluation in the diagnosis and monitoring of parkinson's disease is a primary importance objective in neurology. Furthermore, in many patients next to resting tremor typi...
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ISBN:
(纸本)9781479921317
The availability of an objective clinical evaluation in the diagnosis and monitoring of parkinson's disease is a primary importance objective in neurology. Furthermore, in many patients next to resting tremor typical of the disease are also found other types of tremor as kinetic and postural tremor so making the diagnosis difficult. The ability to classify the different types of tremor specific for each patient through an examination of the instrumental, non-invasive and very simple and fast is a great tool to aid the clinical diagnosis of the disease. Our system meets the above requirements. It consists of an inertial sensor that allows the acquisition of the quantities of interest, and by a series of algorithms able to provide an objective and quantitative assessment of the type and severity of tremor in patients with parkinson's disease. The availability of an objective report on the severity of the disorder developed according to a strict correlation with the valuation provided by the UPDRS scale is a good starting point towards the personalization of care as well as being a useful tool in the analysis of the course of the disease.
A new way of modeling the Smooth Pursuit System (SPS) in humans by means of Volterra series expansion is suggested and utilized together with Gaussian Mixture Models (GMMs) to successfully distinguish between healthy ...
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ISBN:
(纸本)9783952426913
A new way of modeling the Smooth Pursuit System (SPS) in humans by means of Volterra series expansion is suggested and utilized together with Gaussian Mixture Models (GMMs) to successfully distinguish between healthy controls and parkinson patients based on their eye movements. To obtain parsimonious Volterra models, orthonormal function expansion of the Volterra kernels in Laguerre functions with the coefficients estimated by SParse Iterative Covariance-based Estimation (SPICE) is used. A combination of these two techniques is shown to greatly reduce the number of model parameters without significant performance loss. In fact, the resulting models outperform the Wiener models of previous research despite the significantly lower number of model parameters. Furthermore, the results of this study indicate that the nonlinearity of the system is likely to be dynamical in nature, rather than static which was previously presumed. The difference between the SPS in healthy controls and parkinson patients is shown to lie largely in the higher order dynamics of the system. Finally, without the model reduction provided by SPICE, the GMM estimation fails, rendering the model unable to separate healthy controls from parkinson patients.
Gait analysis has a significant role in assessing human's walking pattern. It is generally used in sports science for understanding body mechanics, and it is also used to monitor patients' neuro-disorder relat...
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ISBN:
(纸本)9781479921317
Gait analysis has a significant role in assessing human's walking pattern. It is generally used in sports science for understanding body mechanics, and it is also used to monitor patients' neuro-disorder related gait abnormalities. Traditional marker-based systems are well known for tracking gait parameters for gait analysis, however, it requires long set up time therefore very difficult to be applied in everyday real-time monitoring. Nowadays, there is ever growing of interest in developing portable devices and their supporting software with novel algorithms for gait pattern analysis. The aim of this research is to investigate the possibilities of novel gait pattern detection algorithms for accelerometer-based sensors. In particular, we have used e-AR sensor, an ear-worn sensor which registers body motion via its embedded 3-D accelerometer. Gait data was given semantic annotation using pressure mat as well as real-time video recording. Important time stamps within a gait cycle, which are essential for extracting meaningful gait parameters, were identified. Furthermore, advanced signal processing algorithm was applied to perform automatic feature extraction by signal decomposition and reconstruction. Analysis on real-word data has demonstrated the potential for an accelerometer-based sensor system and its ability to extract of meaningful gait parameters.
Automated clump decomposition is essential for single cell based analysis of fluorescent microscopy images. This paper presents a new method for automatically splitting clumps of cell nuclei in fluorescence microscopy...
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ISBN:
(纸本)9781479921317
Automated clump decomposition is essential for single cell based analysis of fluorescent microscopy images. This paper presents a new method for automatically splitting clumps of cell nuclei in fluorescence microscopy images. Nuclei are first segmented using histogram concavity analysis. Clumps of nuclei are detected by fitting an ellipse to the segmented objects and examining objects where the fitted ellipse does not overlap accurately with the segmented object. These clumps are then further processed to find concave points on the object boundaries. The orientation of the detected concavities is subsequently calculated based on the local shape of the object border. Finally, a graph segmentation based approach is used to pair concavities that represent best candidates for splitting touching nuclei based on properties derived from the local concavity properties. This approach was validated by manual inspection and has shown promising results in the high throughput analysis of HeLa cell images.
The pilot work presented here represents a first step toward implementing advanced strategies to optimize clinical outcomes of deep brain stimulation in parkinson’s disease using systematic data capture an...
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The pilot work presented here represents a first step toward implementing advanced strategies to optimize clinical outcomes of deep brain stimulation in parkinson’s disease using systematic data capture and analysis. The authors reliably predicted clinical outcomes by processing accelerometer data that captured motor responses to changes in deep-brain stimulation parameters. Deep-brain stimulation can help manage parkinson’s symptoms. This department is part of a special issue on implantable electronics.
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