Video Experimental Relacionado

Updated: Feb 21, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Published on: July 24, 2019

Modelo Bi-Cefálico Auto-Atendido para Clasificar Pacientes con Enfermedad de Parkinson con Congelación de Marcha

Shomoita Jahid Mitin^1,2, Rodrigue Rizk², Maximilian Scherer³

¹Biomedical and Translational Sciences, University of South Dakota, Vermillion, South Dakota, USA.

The European journal of neuroscience

|February 19, 2026

Resumen

Un nuevo modelo multimodal que combina electroencefalografía (EEG) y datos descriptivos identifica con precisión a los pacientes con Enfermedad de Parkinson con congelación de marcha (FOG). Este enfoque ofrece un método más objetivo y eficiente para diagnosticar FOG en la Enfermedad de Parkinson (PD).

Conclusiones:

La integración del EEG en reposo con características descriptivas objetivas mejora la precisión de la clasificación de PDFOG+.
La arquitectura multimodal basada en la atención desarrollada ofrece una alternativa escalable y eficiente para la evaluación objetiva de FOG en la EP.
Este enfoque tiene potencial para el monitoreo clínico de rutina y el diagnóstico temprano de la disfunción de la marcha en la Enfermedad de Parkinson.

Palabras clave:

EEG Enfermedad de Parkinson clasificación congelación de marcha aprendizaje automático oscilaciones

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