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| Hauptseite > Publikationsdatenbank > Prediction of depression in Parkinson’s disease by prosodic speech features > print |
| Hauptseite > Publikationsdatenbank > Prediction of depression in Parkinson’s disease by prosodic speech features > print |
| 001 | 1044377 | ||
| 005 | 20250722202240.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2025-03152 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-03152 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Kuhles, Gianna |0 P:(DE-Juel1)187476 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 43. European Workshop on Cognitive Neuropsychology |g EWCN |c Bressanone |d 2025-01-26 - 2025-01-30 |w Italy |
| 245 | _ | _ | |a Prediction of depression in Parkinson’s disease by prosodic speech features |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 500 | _ | _ | |a This study was supported by▪ the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2–1, EI 816/16-1 and EI 816/21-1),▪ the National Institute of Mental Health (R01-MH074457),▪ the Helmholtz Portfolio Theme "Supercomputing and Modeling for the HumanBrain",▪ the Virtual Brain Cloud (EU H2020, no. 826421) &▪ the National Institute on Aging (R01AG067103) |
| 520 | _ | _ | |a Introduction: Parkinson’s disease (PD) is a progressive neurodegenerative disease whichmainly affects functions of the motor system. Depressive disorders (DD) are one of the mostcommon comorbidities in PD (Perez-Toro et al., 2022) and often lead to a decrease in qualityof life (Balestrino & Martinez-Martin, 2017). Previous research revealed a strong relationshipbetween prosodic impairment and DD (Vélez Feijó et al., 2008) which is why recent studieshave investigated whether speech parameters might be suitable as biomarkers for the predictionof depression in PD. However, the variability in feature extraction tools and methodologies hasleft the most predictive prosodic parameters inadequately defined (Perez-Toro et al., 2022). Inour study, we aim to address this gap by identifying reliable, standardised features for predictingdepression in PD.Methods: Healthy subjects (n = 249) and people with PD (n = 121) performed a spontaneousspeech task of 90 s duration. 88 acoustic features related to prosodic functions wereautomatically extracted from these speech samples using the toolbox OpenSMILE. Participantsalso completed the Beck Depression Inventory (BDI-II) to assess depressive symptoms.Prosodic features were used to classify between healthy subjects and people with PD.Additionally, a machine learning regression approach was employed to predict individual BDI-II scores from prosodic speech features and to determine the most significant predictors.Results: Firstly, the classification analysis demonstrated a high level of accuracy (0.997) indistinguishing between healthy subjects and individuals with PD based on the prosodic features.Secondly, the regression analysis revealed an R2 value of 0.024, indicating limited predictivepower for BDI-II scores derived from prosodic features. However, consistent with priorresearch, spectral parameters emerged as the most significant predictors, especially the MelFrequency Cepstral Coefficients, which describe the perceived pitch of the frequency spectrum.Discussion: Although the model demonstrated satisfactory performance in classifying betweenhealthy individuals and those with Parkinson's disease, the rather low accuracy in predictingdepressive disorders (DD) highlights the complexity of using prosodic features for this purpose.While present results need to be validated in larger and diverse samples, they pave the way tothe development of uniform methodologies for the systematic extraction of speech-relatedbiomarkers in PD, which in the long run might have clinical utility for better understanding DDin people with PD.Pérez-Toro PA et al. Speech Commun. (2022) 145:10-20.Balestrino R & Martinez-Martin P J. Neurol. Sci. (2017) 374: 3-8.Vélez Feijó A et al. Neuro. Dis. Treat. (2008) 4:669-674. |
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