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. 2024 Sep 13:18:1449388.
doi: 10.3389/fnhum.2024.1449388. eCollection 2024.

Detecting fatigue in multiple sclerosis through automatic speech analysis

Marcelo Dias  1 , Felix Dörr  1 , Susett Garthof  2 , Simona Schäfer  1 , Julia Elmers  2 , Louisa Schwed  1 , Nicklas Linz  1 , James Overell  3   4 , Helen Hayward-Koennecke  3 , Johannes Tröger  1 , Alexandra König  1   5 , Anja Dillenseger  2 , Björn Tackenberg  3   6 , Tjalf Ziemssen  2
Affiliations

Detecting fatigue in multiple sclerosis through automatic speech analysis

Marcelo Dias et al. Front Hum Neurosci. .

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 (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: automated speech analysis; fatigue; machine learning; multiple sclerosis (MS); speech.

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Conflict of interest statement

AD received personal compensation and travel grants from Biogen, Celgene/Bristol-Myers Squibb, Roche, Janssen-Cilag and Sanofi for speaker activity. TZ reports personal fees for lecturing and consulting from Biogen, BMS, F. Hoffmann-La Roche Ltd, Merck, Novartis, Sanofi, Teva and Almirall; and grants or research support from Biogen, F. Hoffmann-La Roche Ltd, Teva, Sanofi and Novartis. MD, FD, SS, NL, JT, LS and AK are employees of ki:elements GmbH. Nicklas Linz and JT are shareholders of ki:elements. BT, HH-K and JO are employees of F.Hoffmann LaRoche AG, Switzerland. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
(A) Receiver operating characteristic curve (ROC) for the different SVM models. Free speech includes negative and positive storytelling, and picture description. Performance metrics were computed based on a 10-fold cross validation strategy with a 90–10% train-test splits. Black dashed line represents the chance level. (B) Performance metrics for the picture description model as a function of number of features. PD, picture description; SB-C, speech biomarker for cognition (ki:elements). AUC, area under the curve.
Figure 2
Figure 2
(A) Scatter plot showing the partial correlation between FSMC total scores and speech ratio, with the effect of EDSS scores statistically controlled. (B) Scatter plot showing the partial correlation between FSMC cognitive scores and speech ratio, with the effect of EDSS scores statistically controlled. Blue lines represent the line of best fit, with the shaded areas indicating the 95% confidence interval.
See this image and copyright information in PMC

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