Speech parameter and deep learning based approach for the detection of parkinson’s disease

被引：1
作者：
Krishna A. [1 ]
Sahu S.P. [1 ]
Janghel R.R. [1 ]
Singh B.K. [2 ]
机构：
[1] Department of Information and Technology, NIT Raipur, Raipur
[2] Department of BioMedical Engineering, NIT Raipur, Raipur
来源：
Lecture Notes on Data Engineering and Communications Technologies | 2021年 / 66卷
关键词：
Computer-aided system; Convolutional neural network; Deep learning; Parkinson’s disease;
D O I：
10.1007/978-981-16-0965-7_40
中图分类号：
学科分类号：
摘要：
Parkinson’s disease is one of the common chronic and progressive neurodegenerative diseases across the globe. Speech parameters are the most impor-tant indicators that can be used to detect the disease at its early stage. In this article, an efficient approach using Convolutional Neural Network (CNN) is used to predict Parkinson’s disease by using speech parameters that are extracted from the voice recordings. CNN is the most emerging technology that is used for many computer vision tasks. The performance of the approach is discussed and evaluated with the dataset available in the UCI machine learning repository. The dataset will contain the attributes of voice recordings from 80 individuals out of which 40 individuals are Parkinson affected. Three recordings of each individual are used and 44 features are extracted from each recording of a subject. The experimental setup of the proposed approach on the benchmark data has achieved the best testing accuracy of 87.76% when compared with the available ground truth of the dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
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页码：507 / 517
页数：10
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