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ORIGINAL RESEARCH article

Front. Hum. Neurosci.
Sec. Brain Health and Clinical Neuroscience
Volume 18 - 2024 | doi: 10.3389/fnhum.2024.1449388
This article is part of the Research Topic Digital Medicine and Chronic Neurological Disorders View all articles

Detecting Fatigue in Multiple Sclerosis through Automatic Speech Analysis

Provisionally accepted
Marcelo Dias Marcelo Dias 1*Felix Dörr Felix Dörr 1Susett Garthof Susett Garthof 2Simona Schäfer Simona Schäfer 1Julia Elmers Julia Elmers 2Louisa Schwed Louisa Schwed 1Nicklas Linz Nicklas Linz 1James Overell James Overell 3,4Helen Hayward-Koennecke Helen Hayward-Koennecke 3Johannes Tröger Johannes Tröger 1Alexandra König Alexandra König 1Anja Dillenseger Anja Dillenseger 2Björn Tackenberg Björn Tackenberg 3,5Tjalf Ziemssen Tjalf Ziemssen 2
  • 1 ki:elements, Saarbrucken, Germany
  • 2 Center of Clinical Neuroscience, Department of Neurology, University Clinic Carl Gustav Carus Dresden, TU Dresden, Dresden, Germany
  • 3 F. Hoffmann La Roche AG, Basel, Switzerland
  • 4 University of Glasgow, Department of Clinical Neuroscience, Glasgow, United Kingdom
  • 5 Philipps-University, Dept. of Neurology, Marburg, Germany

The final, formatted version of the article will be published soon.

    Multiple sclerosis (MS) is a chronic neuroinflammatory disease characterized by central nervous system demyelination and axonal degeneration. Fatigue affects a major portion of MS patients, significantly impairing their daily activities and quality of life. Despite its prevalence, the mechanisms underlying fatigue in MS are poorly understood, and measuring fatigue remains a challenging task. This study evaluates the efficacy of automated speech analysis in detecting fatigue in MS patients. MS patients underwent a detailed clinical assessment and performed a comprehensive speech protocol. Using features from three different free speech tasks and a proprietary cognition score, our support vector machine model achieved an AUC on the ROC of 0.74 in detecting fatigue. Using only free speech features evoked from a picture description task we obtained an AUC of 0.68. This indicates that specific free speech patterns can be useful in detecting fatigue. Moreover, cognitive fatigue was significantly associated with lower speech ratio in free speech (ρ = -0.283, p = 0.001), suggesting that it may represent a specific marker of fatigue in MS patients. Together, our results show that automated speech analysis, of a single narrative free speech task, offers an objective, ecologically valid and low-burden method for fatigue assessment. Speech analysis tools offer promising potential applications in clinical practice for improving disease monitoring and management.

    Keywords: multiple sclerosis (MS), fatigue2, Speech, Automated speech analysis, Machine learning Font: Italic Formatted: Font: Italic Formatted: Font: Italic Formatted: Font: Italic

    Received: 14 Jun 2024; Accepted: 26 Aug 2024.

    Copyright: © 2024 Dias, Dörr, Garthof, Schäfer, Elmers, Schwed, Linz, Overell, Hayward-Koennecke, Tröger, König, Dillenseger, Tackenberg and Ziemssen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Marcelo Dias, ki:elements, Saarbrucken, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.