Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU141748" target="_blank" >RIV/00216305:26220/21:PU141748 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ACCESS.2021.3119035" target="_blank" >https://doi.org/10.1109/ACCESS.2021.3119035</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2021.3119035" target="_blank" >10.1109/ACCESS.2021.3119035</a>

Result language

angličtina

Original language name

Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease

Original language description

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized decision support systems for PD dysgraphia have been proposed, however, the associated challenges require new approaches for more accurate diagnosis. Therefore, this work adds spectral and cepstral handwriting features to the already-used temporal, kinematic and statistics handwriting features. First, we calculate temporal and kinematic features using displacement; statistic features (SF) using displacement, and horizontal and vertical displacement; spectral (SDF) and cepstral (CDF) using displacement, horizontal and vertical displacement and pressure. Since the employed dataset (PaHaW) contains only 37 PD patients and 38 healthy control subjects (HC), then as the second step, we augment the percentage of the smaller training set to equal the larger. Next, we augment both classes to increase the training patient’s data and added random Gaussian noise in all augmentations. Third, the most relevant features were selected using the modified fast correlation-based filtering method (mFCBF). Finally, autoML is employed to train and test more than ten plain and ensembled classifiers. Experimental results show that adding spectral and cepstral features to temporal, kinematics and statistics features highly improved classification accuracy to 98.57%. Our proposed model, with lower computational complexities, outperforms conventional state-of-the-art models for all tasks, which is 97.62%.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

141599-141610

UT code for WoS article

000711695800001

EID of the result in the Scopus database

2-s2.0-85117143535