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Analysis of institutional authors

Jimenez-Martin, AntonioAuthor

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February 4, 2026
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Machine Learning for Parkinson's Disease Detection: Analyzing Hybrid Voice Data With Spectral, Topological, and Random Matrix Methods

Publicated to: IEEE Open Journal of the Computer Society. 7 314-325 - 2026-01-01 7(), DOI: 10.1109/OJCS.2026.3651318

Authors:

Dominguez-Monterroza, Andy; Caballero, Alfonso Mateos; Jimenez-Martin, Antonio
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Affiliations

Univ Politecn Madrid, Dept Inteligencia Artificial, Grp Anal Decis & Estadist, Madrid 28040, Spain - Author

Abstract

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects both motor and speech functions. Advances in machine learning and signal processing have enabled non-invasive PD detection through voice analysis. This study proposes a comprehensive mathematical framework for PD classification that integrates topological, statistical, and spectral representations of speech signals. The framework combines topological descriptors derived from persistent homology, statistical measures based on random matrix theory, and spectral features extracted from frequency-domain analysis to capture complementary information about vocal dynamics. A hybrid training strategy was employed, using synthetic speech data generated from real recordings to train the models, while real samples were reserved exclusively for evaluation. Experimental results demonstrate that spectral features, particularly when fused with statistical descriptors, yield the highest discriminative power, achieving 98.00% accuracy and 97.98% F1-score with a multi-layer perceptron classifier. In contrast, topological descriptors provided limited standalone performance, serving instead as complementary components that enrich the overall representation. The findings highlight the potential of combining diverse mathematical representations to improve speech-based PD detection, especially in scenarios with limited access to clinically annotated data.
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Keywords

AccuracyAcousticsClassificationDiseasesFeature extractionMachine learningNoiseParkinson's diseaseRandom matrix theorySpectral featuresSpeechSpeech analysisSpeech synthesisSynthetic dataTopological data analysisTraining

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Open Journal of the Computer Society due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2026, it was in position 14/258, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Information Systems. Notably, the journal is positioned above the 90th percentile.

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 3 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/94363/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 27
  • Downloads: 28
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Dominguez-Monterroza, Andy) and Last Author (JIMENEZ MARTIN, ANTONIO).

the author responsible for correspondence tasks has been Caballero, Alfonso Mateos.

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Awards linked to the item

This work was supported by MICIU/AEI/10.13039/501100011033 under Grant PID2021-122209OB-C31, Grant PID2024-155179NB-C22, and Grant RED2022-134540-T.
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