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

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.