Machine Learning Using Speech Utterances for Parkinson Disease Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F18%3A00326022" target="_blank" >RIV/68407700:21460/18:00326022 - isvavai.cz</a>

Result on the web

<a href="https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/4881" target="_blank" >https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/4881</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Machine Learning Using Speech Utterances for Parkinson Disease Detection

Original language description

Pathophysiological recordings of patients measured from various testing methods are frequently used in the medical field for determining symptoms as well as for probability prediction for selected diseases. There are numerous symptoms among the Parkinson’s disease (PD) population, however changes in speech and articulation – is potentially the most significant biomarker. This article is focused on PD diagnosis classification based on their speech signals using pattern recognition methods (AdaBoost, Bagged trees, Quadratic SVM and k-NN). The dataset investigated in the article consists of 30 PD and 30 HC individuals’ voice measurements, with each individual being represented with 2 recordings within the dataset. Training signals for PD and HC underwent an extraction of relatively well-discriminating features relating to energy and spectral speech properties. Model implementations included a 5-fold cross validation. The accuracy of the values obtained employing the models was calculated using the confusion matrix. The average value of the overall accuracy = 82.3 % and averaged AUC = 0.88 (min. AUC = 0.86) on the available data.

Czech name

—

Czech description

—

Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Lékař a technika – Clinician and Technology

ISSN

0301-5491

e-ISSN

2336-5552

Volume of the periodical

48

Issue of the periodical within the volume

2

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

6

Pages from-to

66-71

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85057083859