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Early Detection of Parkinson’s Disease by Neural Network Models | IEEE Journals & Magazine | IEEE Xplore

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The aging of today’s society is associated with an increasing number of patients suffering from neurodegenerative disorders. One of these disorders is Parkinson’s disease (PD), and current estimates indicate that the number of people with PD will rise more than twofold, from 4 million in 2005 to 9 million by 2030 [1]. The clinical presentations of PD include progressively slowing movements, limb rigidity, rest tremor, and posture instability [2]. Unfortunately, even those patients who receive dopaminergic treatment or deep brain stimulation still deteriorate with increasing age, and their mortality rate is two- to three-fold higher than that of the general population [3]. Therefore, recognizing PD in its early stage is critical for initiating proper treatments to decrease morbidity and ease the medical burden in the elderly. The clinical severity of PD can be divided into five stages, called the Hoehn-Yahr Stages I–V [4]. In Stage I, the patients experience unilateral symptoms, such as

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