English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Parkinson’s disease (PD) is one of the neurodegenerative diseases. The neuronal loss caused by this disease leads to symptoms such as lack of initiative, depressive states, psychological disorders, and impairment of cognitive functions as well as voice dysfunctions. This paper aims to propose a system of automatic recognition of Parkinson’s disease by voice analysis. In this system, we are based on a database of 38 recordings, 20 people with Parkinson’s disease and 18 healthy people pronounce the vowel /a/.at first, we have decomposed the vocal signal of each patient by the Empirical Mode Decomposition (EMD), then, we extract from 1 to 12 coefficients of the Mel Frequency Cepstral Coefficients (MFCC), to obtain the voiceprint from each voice sample, we compressed the frames by computing their average value. At the end of the classification, we have used the validation scheme “holdout” as well as the K-nearest neighbor (KNN) classifier, the performance of this classification gives accuracy up to 86,67% when applied to 80% of the database as training data.

itm web of conferences

Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier