ArticleFrontiers in human neuroscience2024
Detecting fatigue in multiple sclerosis through automatic speech analysis.
Article in Frontiers in human neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Listening to MS: AI-assisted speech analysis for diagnosis and fatigue prediction (COMMITMENT).Frontiers in digital health · 2026Article
- MSPEECH (multiple sclerosis monitoring through speech interaction in clinic and at home): a Living Lab study protocol for co-created, speech-based digital biomarkers in multiple sclerosis.Frontiers in digital health · 2026Article
- From "invisible" to "audible": Features extracted during simple speech tasks classify patient-reported fatigue in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
- Neurological complications in oncology and their monitoring and management in clinical practice: a narrative review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Review
- Speech-based concussion detection in athletes using Mel-spectrograms and convolutional neural networks.Frontiers in neurologyArticle
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Authors and funding
14 authors.
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Abstract
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 (
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