Use of Laughter for the Detection of Parkinson’s Disease: Feasibility Study for Clinical Decision Support Systems, Based on Speech Recognition and Automatic Classification Techniques

Miguel Terriza, Jorge Navarro, Irene Retuerta, Nuria Alfageme, Ruben San-Segundo, George Kontaxakis, Elena Garcia-Martin, Pedro C. Marijuan and Fivos Panetsos ()
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Miguel Terriza: Neuro-Computing & Neuro-Robotics Research Group, Complutense University of Madrid, 28040 Madrid, Spain
Jorge Navarro: Department of Economic Structure, CASETEM Research Group, Faculty of Economy, University of Zaragoza, 50009 Zaragoza, Spain
Irene Retuerta: Independent Researchers, Affiliated to Bioinformation and Systems Biology Group, Aragon Health Sciences Institute (IACS-IIS Aragon), 50009 Zaragoza, Spain
Nuria Alfageme: Neuro-Computing & Neuro-Robotics Research Group, Complutense University of Madrid, 28040 Madrid, Spain
Ruben San-Segundo: Speech Technology Group, Information Processing and Telecommunications Center, 28040 Madrid, Spain
George Kontaxakis: Biomedical Image Technologies Group, Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Elena Garcia-Martin: Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain
Pedro C. Marijuan: Independent Researchers, Affiliated to Bioinformation and Systems Biology Group, Aragon Health Sciences Institute (IACS-IIS Aragon), 50009 Zaragoza, Spain
Fivos Panetsos: Neuro-Computing & Neuro-Robotics Research Group, Complutense University of Madrid, 28040 Madrid, Spain

IJERPH, 2022, vol. 19, issue 17, 1-15

Abstract: Parkinson’s disease (PD) is an incurable neurodegenerative disorder which affects over 10 million people worldwide. Early detection and correct evaluation of the disease is critical for appropriate medication and to slow the advance of the symptoms. In this scenario, it is critical to develop clinical decision support systems contributing to an early, efficient, and reliable diagnosis of this illness. In this paper we present a feasibility study for a clinical decision support system for the diagnosis of PD based on the acoustic characteristics of laughter. Our decision support system is based on laugh analysis with speech recognition methods and automatic classification techniques. We evaluated different cepstral coefficients to identify laugh characteristics of healthy and ill subjects combined with machine learning classification models. The decision support system reached 83% accuracy rate with an AUC value of 0.86 for PD–healthy laughs classification in a database of 20,000 samples randomly generated from a pool of 120 laughs from healthy and PD subjects. Laughter could be employed for the efficient and reliable detection of PD; such a detection system can be achieved using speech recognition and automatic classification techniques; a clinical decision support system can be built using the above techniques. Significance: PD clinical decision support systems for the early detection of the disease will help to improve the efficiency of available and upcoming therapeutic treatments which, in turn, would improve life conditions of the affected people and would decrease costs and efforts in public and private healthcare systems.

Keywords: machine learning; Parkinson´s disease; PD; biomarker; laugh; clinical decision support systems; automatic classification techniques; artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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