A Proof-of-Concept Development on Speech Analysis for Concussion Detection
Date
Authors
Silva, Upeka De
Madanian, Samaneh
Narayanan, Ajit
Templeton, John Michael
Poellabauer, Christian
Schneider, Sandra L
Rubaiat, Rahmina
Supervisor
Item type
Journal Article
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Volume Title
Publisher
IOS Press
Abstract
Speech signal analysis to support objective clinical decision-making has gained immense interest, especially in neurological disorders. This research assessed the feasibility of speech analysis on the detection of concussions. Using a speech dataset from 82 concussed and 82 healthy participants, we extracted two speech feature sets focusing on Mel Frequency Cepstral Coefficients (MFCCs) to characterize speech articulation. A machine learning pipeline was developed to discriminate concussion speech from healthy speech by applying Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree (DT) classifiers. All three classifiers trained on the MFCC-based feature set achieved Matthew's correlation coefficient score above 0.5 on the holdout data set. DT model achieved a 78% sensitivity and 75% specificity. The findings of this research serve as proof-of-concept for speech analysis of concussion detection.Description
Keywords
Concussion Detection, Machine Learning, Speech Analysis, 0807 Library and Information Studies, 1117 Public Health and Health Services, Medical Informatics, 4203 Health services and systems, 4601 Applied computing
Source
Studies in Health Technology and Informatics ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, Volume 329: MEDINFO 2025 — Healthcare Smart × Medicine Deep. 1008-1012. doi: 10.3233/SHTI250991
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© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).