A Proof-of-Concept Development on Speech Analysis for Concussion Detection.

Journal: Studies in health technology and informatics
Published Date: Aug 7, 2025

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.

Authors

  • Upeka De Silva
    Department of Data Science and Artificial Intelligence, AUT, New Zealand.
  • Samaneh Madanian
    Computer Science & Software Engineering, Auckland University of Technology, Auckland 1010, New Zealand.
  • Ajit Narayanan
    Department of Computer Science and Software Engineering, AUT, New Zealand.
  • John Michael Templeton
    University of South Florida - Department of Computer Science and Engineering, 4202 E Fowler Ave, Tampa, FL, 33620, USA. Electronic address: jtemplet@usf.edu.
  • Christian Poellabauer
    Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States.
  • Sandra L Schneider
    Department of Communication Sciences and Disorders, Saint Mary's College, Notre Dame, IN, USA.
  • Rahmina Rubaiat
    Florida International University, Miami, FL 33199, USA.

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