Detail publikace

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

NOLAZCO-FLORES, J. A. FAUNDEZ-ZANUY, M. DE LA CUEVA, V. M. MEKYSKA, J.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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%.

Klíčová slova

Parkinson’s disease; dysgraphia; online handwriting; feature extraction; data augmentation; autoML

Autoři

NOLAZCO-FLORES, J. A.; FAUNDEZ-ZANUY, M.; DE LA CUEVA, V. M.; MEKYSKA, J.

Vydáno

8. 10. 2021

ISSN

2169-3536

Periodikum

IEEE Access

Ročník

9

Číslo

1

Stát

Spojené státy americké

Strany od

141599

Strany do

141610

Strany počet

12

URL

https://doi.org/10.1109/ACCESS.2021.3119035

Plný text v Digitální knihovně

http://hdl.handle.net/11012/204025

BibTex

@article{BUT172708,
  author="Juan A. {Nolazco-Flores} and Marcos {Faúndez Zanuy} and V.M. {De La Cueva} and Jiří {Mekyska}",
  title="Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease",
  journal="IEEE Access",
  year="2021",
  volume="9",
  number="1",
  pages="141599--141610",
  doi="10.1109/ACCESS.2021.3119035",
  issn="2169-3536",
  url="https://doi.org/10.1109/ACCESS.2021.3119035"
}