This study presents a new Parkinson's disease diagnosis technique based on wavelets extracted features and machine learning paradigms. The present-day diagnosis techniques suffer from low diagnosis accuracy and also require the patient to go to a medical facility, where the diagnosis is done by a specialist. In this work, we propose an automatic diagnosis method where by, all the patient has to do is to type some keys on their keyboard, and the algorithm will calculate the latency time, flight time and hold time of each key pressed, to make a diagnosis of Parkinson's disease. We use several wavelets to extract some features that are classified into Parkinson's disease or non-Parkinson's disease. The results are very encouraging and we obtain a classification accuracy of up to 100% in some of the cases, using a tenfold crossvalidation technique. Wavelets are a tool that can be used to complement and improve the detection of Parkinson's disease. These results will permit the amelioration of some state-of-the-art methods which use a similar technique to detect Parkinson's disease.
In this study, we present a new family of discrete wavelets which are constructed with the help of Laguerre polynomials and the Daubechies biorthogonal wavelets construction method. Our aim is to propose the discrete version of some previously constructed continuous Laguerre wavelets and also to present a method of discrete wavelets construction by several iterations. With this scheme, we use two different sets of finite impulse response filters for the signal decomposition and their duals for reconstruction. The quadruplet finite impulse response filters respect the anti-aliasing and the perfect reconstruction conditions, and at the same time, they resemble as much as possible the continuous Laguerre wavelets when using the cascade algorithm. We use the mean squared error, the maximum deviation, and the standard deviation to quantify the similarity between the continuous Laguerre wavelets and the constructed discrete Laguerre wavelets. The results show that, they are both the same wavelets due to the small nature of these parameters. Our method is important because, it can permit the determination of the finite impulse response filter coefficients corresponding to many other continuous wavelets.
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