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Laughter

An example of behavioral statistical model: Diagnosis of depression through the analysis of laughter


During 2011 and 2012 I participated in collaboration with P. Marijuan, R. del Moral and J. Navarro from 'Grupo de Investigación de Bioinformación y Biología de Sistemas' Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza (Spain), performing the statistical model in the following project [11c and 12c]. Laughter may be applied as a diagnostic tool in the onset and evolution of depression and, potentially, of neuropsychiatric pathologies. We registered laughs of depressed patients (n=30) and healthy controls (n=20), in total 934 laughs (517 from patients and 417 from controls). All patients were tested by the Hamilton Depression Rating Scale (HDRS). The processing was made in Matlab, with calculation of 8 variables per laugh plosive. General and discriminant statistical analysis distinguished patients, controls, gender, and the association between laughter and HDRS test. Depressed patients and healthy controls differed significantly on the type of laughter, with 88% efficacy. According to the Hamilton scale, 85.47% of the samples were correctly classified in males, and 66.17% in women, suggesting a tight relationship between laughter and the depressed condition.

Right Figure .- Sonogram of laughter recorded from (a) depressed patient and (b) healthy or normal control subject.



In 2013 we conducted an university spin-off project as result of a collaboration among three institutions, the University Complutense of Madrid, the 'Grupo de Investigación de Bioinformación y Biología de Sistemas' (IACS) and the University of Zaragoza. Our group was selected to participate in the 'Second edition of the Entrepreneur Lab' during 2013 in the Madrid Science Park. One goal of the project was to design a software for automatic analysis of laughter: once laughter is recorded- being the stimulus a funny video clip, the software generates the sonogram, extracts the values of diagnostic variables  and performs the diagnosis using advanced techniques and artificial intelligence methods. An option is to distribute the software as embedded system to be incorporated into specialized medical equipment for hospitals as well as a downloadable app for mobile telephones or via Internet as e-Health service.



This project had an impact in the media in Spain being published some news in local newspapers [see news, in Spanish].

Bottom Figure.- December 11. 2011 'Heraldo Domingo' 5

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