Authors:
Manuel Gil-Martín
;
Sergio Esteban-Romero
;
Fernando Fernández-Martínez
and
Rubén San-Segundo
Affiliation:
Grupo de Tecnología del Habla y Aprendizaje Automático (T.H.A.U. Group), Information Processing and Telecommunications Center, E.T.S.I. de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
Keyword(s):
Parkinson’s Disease Detection, Inertial Signals, Fast Fourier Transform, Tremor Detection, Convolutional Neural Networks, Window Size.
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 L
OSO scenario.
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