A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing

Manuel Gil-Martín, Sergio Esteban-Romero, Fernando Fernández-Martínez, Rubén San-Segundo

2024

Abstract

When developing deep learning systems for Parkinson’s Disease (PD) detection using inertial sensors, a comprehensive analysis of some key factors, including data distribution, signal processing domain, number of sensors, and analysis window size, is imperative to refine tremor detection methodologies. Leveraging the PD-BioStampRC21 dataset with accelerometer recordings, our state-of-the-art deep learning architecture extracts a PD biomarker. Applying Fast Fourier Transform (FFT) magnitude coefficients as a preprocessing step improves PD detection in Leave-One-Subject-Out Cross-Validation (LOSO CV), achieving 66.90% accuracy with a single sensor and 6.4-second windows, compared to 60.33% using raw samples. Integrating information from all five sensors boosts performance to 75.10%. Window size analysis shows that 3.2-second windows of FFT coefficients from all sensors outperform shorter or longer windows, with a window-level accuracy of 80.49% and a user-level accuracy of 93.55% in a LOSO scenario.

Paper Citation

in Harvard Style

Gil-Martín M., Esteban-Romero S., Fernández-Martínez F. and San-Segundo R. (2024). A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 462-469. DOI: 10.5220/0012360100003636

in Bibtex Style

@conference{icaart24,
author={Manuel Gil-Martín and Sergio Esteban-Romero and Fernando Fernández-Martínez and Rubén San-Segundo},
title={A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={462-469},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012360100003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Comprehensive Analysis of Parkinson’s Disease Detection Through Inertial Signal Processing
SN - 978-989-758-680-4
AU - Gil-Martín M.
AU - Esteban-Romero S.
AU - Fernández-Martínez F.
AU - San-Segundo R.
PY - 2024
SP - 462
EP - 469
DO - 10.5220/0012360100003636
PB - SciTePress