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, Posture Insights, Lying, Sitting, Convolutional Neural Networks.
Abstract:
In the development of deep learning systems aimed at detecting Parkinson's Disease (PD) using inertial
sensors, some aspects could be essential to refine tremor detection methodologies in realistic scenarios. This
work analyses the effect of the subjects’ posture during tremor recordings and the required amount of data
to assess a proper PD detection in a Leave-One-Subject-Out Cross-Validation (LOSO CV) scenario. We
propose a deep learning architecture that learns a PD biomarker from accelerometer signals to classify
subjects between healthy and PD patients. This study uses the PD-BioStampRC21 dataset, containing
accelerometer recordings from healthy and PD participants equipped with five inertial sensors. An
increment of performance was obtained when using sitting windows compared to using lying windows for
Fast Fourier Transform (FFT) input signal domain. Moreover, using 5 minutes per subject could be
sufficient to properly evaluate the PD status of a patient without losin
g performance, reaching a windowlevel accuracy of 77.71 ± 1.07 % and a user-level accuracy of 87.10 ± 11.80 %. Furthermore, a knowledge
transfer could be performed when training the system with sitting instances and testing with lying examples,
indicating that the sitting activity contains valuable information that allows an effective generalization to
lying instances.
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