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Computer keyboard interaction as an indicator of early Parkinson's disease.
Giancardo, L; Sánchez-Ferro, A; Arroyo-Gallego, T; Butterworth, I; Mendoza, C S; Montero, P; Matarazzo, M; Obeso, J A; Gray, M L; Estépar, R San José.
Afiliación
  • Giancardo L; Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Sánchez-Ferro A; Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Arroyo-Gallego T; HM Hospitales - Centro Integral en Neurociencias HM CINAC, Móstoles, Madrid, Spain.
  • Butterworth I; CEU San Pablo University, Campus de Moncloa, Calle Julián Romea, 18, 28003 Madrid, Spain.
  • Mendoza CS; Centro de Investigaci ´on Biom´edica en Red, Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.
  • Montero P; Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain.
  • Matarazzo M; Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Obeso JA; Universidad Politécnica de Madrid, Spain.
  • Gray ML; Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Estépar RS; Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sci Rep ; 6: 34468, 2016 10 05.
Article en En | MEDLINE | ID: mdl-27703257
Parkinson's disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Interfaz Usuario-Computador / Modelos Biológicos / Actividad Motora Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Interfaz Usuario-Computador / Modelos Biológicos / Actividad Motora Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Sci Rep Año: 2016 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido