A Review on Parkinson Disease ClassifierUsing Patient Voice Features

Priyanka Holkar, P. Gatti, Sonali Meher, P. Sable
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引用次数: 1

Abstract

Parkinson’s disease (PD) known as chronic and progressive movement disorder, means that symptoms continues and becomes worst over time.PD affects on person’s moves, also affects how they speak and write. After Alzheimer’s disease, around whole world 6.3 million people live with Parkinson’s disease which makes it the second most common neurological disorder. The cause of disease is unknown, and also there is presently no cure and no treatment options such as medication and surgery to manage its syndromes. Approximately 90% of PD patients have suffered speech difficultiesi.e., dysphonia which is impaired speech production and dysarthria is referred as speech articulation difficulties. These mobility deficits are difficult to treat with drugs or neurosurgery. Parkinson disease people must visit clinician to track their progressions regularly. It will become simple process to anticipate harshness of disease with the help of voice recording of patients. This can be achieved by using Hoehn Yahr Score and Parkinson disease Rating Scale (PDRS) Score. Combination of machine learning algorithms are used for classification of voice features according to severities of disease.
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基于患者语音特征的帕金森病分类研究进展
帕金森病(PD)被称为慢性进行性运动障碍,意味着症状持续并随着时间的推移变得更糟。PD会影响人的动作,也会影响他们说话和写作的方式。继阿尔茨海默病之后,全世界有630万人患有帕金森病,这使其成为第二大最常见的神经系统疾病。该病的病因尚不清楚,目前也没有治愈方法,也没有药物和手术等治疗选择来控制其综合征。大约90%的PD患者有语言障碍。在美国,发音障碍是一种言语产生障碍和构音障碍,被称为言语发音困难。这些活动障碍很难用药物或神经外科手术来治疗。帕金森氏症患者必须定期拜访临床医生,跟踪病情进展。借助患者的语音记录,预测疾病的严重性将成为一个简单的过程。这可以通过使用Hoehn Yahr评分和帕金森病评定量表(PDRS)评分来实现。结合机器学习算法,根据疾病的严重程度对语音特征进行分类。
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