{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:59Z","timestamp":1755219839651,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3233\/shti250991","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:30Z","timestamp":1754566650000},"source":"Crossref","is-referenced-by-count":0,"title":["A Proof-of-Concept Development on Speech Analysis for Concussion Detection"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6913-3464","authenticated-orcid":false,"given":"Upeka De","family":"Silva","sequence":"first","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, AUT, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samaneh","family":"Madanian","sequence":"additional","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, AUT, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajit","family":"Narayanan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, AUT, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John Michael","family":"Templeton","sequence":"additional","affiliation":[{"name":"University of South Florida, FL 33620, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Poellabauer","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, FL 33199, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra L.","family":"Schneider","sequence":"additional","affiliation":[{"name":"Saint Mary\u2019s College, Notre Dame, IN 46556, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahmina","family":"Rubaiat","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, FL 33199, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250991","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:30Z","timestamp":1754566650000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250991"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250991","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}