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Giancardo L, Sánchez-Ferro A, Arroyo-Gallego T, Butterworth I, Mendoza CS, Montero P, Matarazzo M, Obeso JA, Gray ML, Estépar RSJ. Author Correction: Computer keyboard interaction as an indicator of early Parkinson's disease. Sci Rep 2018; 8:15227. [PMID: 30327480 PMCID: PMC6191416 DOI: 10.1038/s41598-018-32121-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- L Giancardo
- Madrid-MIT M+ Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - A Sánchez-Ferro
- Madrid-MIT M+ Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.,HM Hospitales - Centro Integral en Neurociencias HM CINAC, Móstoles, Madrid, Spain.,CEU San Pablo University, Campus de Moncloa, Calle Julián Romea, 18, 28003, Madrid, Spain.,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.,Instituto de Investigación Hospital 12 de Octubre (i+ 12), Madrid, Spain
| | - T Arroyo-Gallego
- Madrid-MIT M+ Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.,Universidad Politécnica de Madrid, Madrid, Spain
| | - I Butterworth
- Madrid-MIT M+ Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - C S Mendoza
- Madrid-MIT M+ Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P Montero
- Movement disorders unit, Hospital Clinico San Carlos, Madrid, Spain
| | - M Matarazzo
- HM Hospitales - Centro Integral en Neurociencias HM CINAC, Móstoles, Madrid, Spain.,CEU San Pablo University, Campus de Moncloa, Calle Julián Romea, 18, 28003, Madrid, Spain.,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.,Instituto de Investigación Hospital 12 de Octubre (i+ 12), Madrid, Spain
| | - J A Obeso
- HM Hospitales - Centro Integral en Neurociencias HM CINAC, Móstoles, Madrid, Spain.,CEU San Pablo University, Campus de Moncloa, Calle Julián Romea, 18, 28003, Madrid, Spain.,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - M L Gray
- Madrid-MIT M+ Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.,The Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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Giancardo L, Sánchez-Ferro A, Arroyo-Gallego T, Butterworth I, Mendoza CS, Montero P, Matarazzo M, Obeso JA, Gray ML, Estépar RSJ. Computer keyboard interaction as an indicator of early Parkinson's disease. Sci Rep 2016; 6:34468. [PMID: 27703257 PMCID: PMC5050498 DOI: 10.1038/srep34468] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 09/12/2016] [Indexed: 12/24/2022] Open
Abstract
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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Affiliation(s)
- L Giancardo
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - A Sánchez-Ferro
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.,HM Hospitales - Centro Integral en Neurociencias HM CINAC, Móstoles, Madrid, Spain.,CEU San Pablo University, Campus de Moncloa, Calle Julián Romea, 18, 28003 Madrid, Spain.,Centro de Investigaci ´on Biom´edica en Red, Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.,Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain
| | - T Arroyo-Gallego
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.,Universidad Politécnica de Madrid, Spain
| | - I Butterworth
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - C S Mendoza
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P Montero
- Movement disorders unit, Hospital Clinico San Carlos, Madrid, Spain
| | - M Matarazzo
- HM Hospitales - Centro Integral en Neurociencias HM CINAC, Móstoles, Madrid, Spain.,CEU San Pablo University, Campus de Moncloa, Calle Julián Romea, 18, 28003 Madrid, Spain.,Centro de Investigaci ´on Biom´edica en Red, Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain.,Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain
| | - J A Obeso
- HM Hospitales - Centro Integral en Neurociencias HM CINAC, Móstoles, Madrid, Spain.,CEU San Pablo University, Campus de Moncloa, Calle Julián Romea, 18, 28003 Madrid, Spain.,Centro de Investigaci ´on Biom´edica en Red, Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - M L Gray
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.,The Institute of Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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Giancardo L, Sánchez-Ferro A, Butterworth I, Mendoza CS, Hooker JM. Psychomotor impairment detection via finger interactions with a computer keyboard during natural typing. Sci Rep 2015; 5:9678. [PMID: 25882641 PMCID: PMC5381750 DOI: 10.1038/srep09678] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 03/12/2015] [Indexed: 11/09/2022] Open
Abstract
Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population.
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Affiliation(s)
- L Giancardo
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - A Sánchez-Ferro
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - I Butterworth
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - C S Mendoza
- Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - J M Hooker
- 1] Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139 [2] Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129
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Mendoza CS, Washko GR, Ross JC, Diaz AA, Lynch DA, Crapo JD, Silverman EK, Acha B, Serrano C, Estépar RSJ. EMPHYSEMA QUANTIFICATION IN A MULTI-SCANNER HRCT COHORT USING LOCAL INTENSITY DISTRIBUTIONS. Proc IEEE Int Symp Biomed Imaging 2012:474-477. [PMID: 23743800 PMCID: PMC3670097 DOI: 10.1109/isbi.2012.6235587] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
This article investigates the suitability of local intensity distributions to analyze six emphysema classes in 342 CT scans obtained from 16 sites hosting scanners by 3 vendors and a total of 9 specific models in subjects with Chronic Obstructive Pulmonary Disease (COPD). We propose using kernel density estimation to deal with the inherent sparsity of local intensity histograms obtained from scarcely populated regions of interest. We validate our approach by leave-one-subject-out classification experiments and full-lung analyses. We compare our results with recently published LBP texture-based methodology. We demonstrate the efficacy of using intensity information alone in multi-scanner cohorts, which is a simpler, more intuitive approach.
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