This paper is in the following e-collection/theme issue:

Neurotech Innovations, Diagnostic Tools and Techniques (51) mHealth for Data Collection and Research (1178) Computer-Aided Diagnosis (235) Digital Biomarkers and Digital Phenotyping (268) Dementia and Cognitive Decline (670) Voice Phenotyping and Vocal Biomarkers (12)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64624, first published .
Exploring Speech Biosignatures for Traumatic Brain Injury and Neurodegeneration: Pilot Machine Learning Study

Exploring Speech Biosignatures for Traumatic Brain Injury and Neurodegeneration: Pilot Machine Learning Study

Exploring Speech Biosignatures for Traumatic Brain Injury and Neurodegeneration: Pilot Machine Learning Study

Authors of this article:

Rahmina Rubaiat1 Author Orcid Image ;   John Michael Templeton2 Author Orcid Image ;   Sandra L Schneider3 Author Orcid Image ;   Upeka De Silva4 Author Orcid Image ;   Samaneh Madanian4 Author Orcid Image ;   Christian Poellabauer1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Sun L, Tseng C, Lin K, Chen P. Hybrid preprocessing and ensemble classification for enhanced detection of Parkinson's disease using multiple speech signal databases. DIGITAL HEALTH 2025;11 View
  2. Perchoux C, Topalian N, Klein S, Chaix B, Tharrey M, Röcke C, Gerber P, Klein O, Missling A, Omrani H, Helbich M, Van Dyck D, Kestens Y, Dijst M, Fagherazzi G. Geographic environments, daily activities and stress in Luxembourg (the FragMent study): a protocol combining map-based questionnaires, geographically explicit ecological momentary assessment and vocal biomarkers of stress. BMJ Open 2025;15(9):e105499 View