Publicaciones de conferencias: comunicaciones, ponencias, pósters, etc (conferenceObject). 2024

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

Archivo Digital UPM
oai:oa.upm.es:80570
Archivo Digital UPM
  • Gil Martín, Manuel
  • Esteban Romero, Sergio
  • Fernández Martínez, Fernando
  • San Segundo Hernández, Rubén
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.
 

DOI: https://oa.upm.es/80570/
Archivo Digital UPM
oai:oa.upm.es:80570

HANDLE: https://oa.upm.es/80570/
Archivo Digital UPM
oai:oa.upm.es:80570
 
Ver en: https://oa.upm.es/80570/
Archivo Digital UPM
oai:oa.upm.es:80570

1033