Bio-Inspired Deep Learning for Parkinson's Disease Detection: A Comparative Study Based on Vocal Biomarkers and Archimedean Spiral Analysis.

Overview

1. Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania; (O.-P.S.); (M.M.)

Abstract

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide, and its early diagnosis remains a major challenge due to reliance on subjective clinical assessments. This study proposes a bio-inspired computational framework for automatic PD detection that draws explicit architectural inspiration from two biological systems: the hierarchical tonotopic organization of the human auditory cortex, which motivates the design of a 1D Convolutional Neural Network (CNN) for vocal biomarker analysis, and the basal ganglia–cerebellar motor control circuit, which motivates the selection and design of features extracted from Archimedean spiral drawing tasks. Unlike previous studies that apply standard machine learning techniques without grounding architectural choices in biological mechanisms, the proposed framework establishes a direct mapping between neural processing pathways and model design decisions. A Support Vector Machine (SVM) classifier evaluated on the Kaggle vocal dataset achieved 87% test accuracy with no overfitting, outperforming AdaBoost, Random Forest, KNN, XGBoost, and Decision Trees in terms of generalization. The 1D CNN applied to UCI spiral drawing data achieved 85% test accuracy, with overfitting behavior addressed through architectural regularization strategies including early stopping. A conceptual multimodal fusion architecture integrating both modalities is proposed as a direction for future experimental validation; it was not implemented or experimentally validated within the present study. The primary novelty of the framework resides in this explicit biomimetic grounding, which distinguishes it from existing performance-driven approaches. Results confirm that biologically grounded computational models constitute promising objective decision-support tools for early PD diagnosis.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• archive.ics.uci.edu/dataset/395 — at archive.ics.uci.edu; found in “Data Availability Statement”
• kaggle.com/datasets/vikasukani — at Kaggle; found in “Data Availability Statement”

Data Availability Statement

The vocal dataset is publicly available on Kaggle at: https://www.kaggle.com/datasets/vikasukani/parkinsons-disease-data-set (accessed on 22 October 2025). The spiral drawing dataset is publicly available at the UCI Machine Learning Repository at: https://archive.ics.uci.edu/dataset/395/parkinson+disease+spiral+drawings+using+digitized+graphics+tablet (accessed on 22 December 2025). No new data were created in this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 9 keywords, 48 references.

Cite

This paper

Stan, O.-P., Misaros, M., & Miclea, L.-C. (2026). Bio-Inspired Deep Learning for Parkinson's Disease Detection: A Comparative Study Based on Vocal Biomarkers and Archimedean Spiral Analysis. Biomimetics (Basel, Switzerland), 11(6), 369. https://doi.org/10.3390/biomimetics11060369

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