Process (with Sphinx) Applied to mTBI mobile application Results: On-Device (iPad mini, iOS 7.0.4) results in speech string: “Six Five Five Eight Seven Four Zero” Data Collection (ND Bengal Bouts): Data Analysis Workflow (pilot) • Extract eight acoustic features for each vowel sound • Design and evaluate many SVM classifiers • Identify feature groups that recall all concussed recordings with minimal false positives Planning for on-device processing • Goals: speed, accuracy, robustness • Embedded speech/audio software (e.g., CMU Sphinx) for speech segmentation and feature extraction • Possible cloud service mTBIs are a high profile problem in sports at all levels • Common in contact sports (~300,000 per year) [1] • Early (sideline) detection may help to avoid important to avoid acute and chronic problems (Secondary Impact Syndrome, Parkinson’s disease, …) mTBIs may affect speech • Brain injuries affect coordination of facial muscles used to articulate phonemes • Can cause slower reaction times and slurred speech [2] The Project • Collect multiple speech samples from athletes (baseline, after practice, after hit, …) using a mobile app • Process speech signal to obtain acoustic features that can classify injury state USING SPEECH AS A BIOMARKER TO DETECT MILD TRAUMATIC BRAIN INJURIES Nikhil Yadav, Christian Poellabauer, Patrick Flynn Dept. of Computer Science & Engineering Advanced Diagnostics & Therapeutics Initiative References:[1] Sports related recurrent brain injuries, MMWR MorbMortal Wkly Rep, vol. 6, pp. 224 - 227, 1997. [2] W. Ziegler and D.V. Cramon, Spastic dysarthria after acquired brain injury: An acoustic study, International Journal of Language & Communication Disorders, vol.21, no.2, pp. 173 - 187, 1986. Acknowledgements: This research is funded by the Advanced Diagnostics and Therapeutics Initiative at the University of Notre Dame and by the NFL/GE Head Health Challenge. Recognized Word Start Time End TIme “Sixth” 0.31 s 0.94 s “I” 0.94 s 1.17 s “Five” 1.17 s 1.95 s “Eight” 1.95 s 2.23 s “Seven” 2.23 s 2.74 s “Four” 2.74 s 3.07 s “Zero” 3.07 s 3.59 s

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Page 1: USING SPEECH AS A BIOMARKER TO DETECT MILD TRAUMATIC …

Process (with Sphinx) Applied to mTBI mobile application Results: On-Device (iPad mini, iOS 7.0.4) results in speech string: “Six Five Five Eight Seven Four Zero”

Data Collection (ND Bengal Bouts): Data Analysis Workflow (pilot) •  Extract eight acoustic features for each

vowel sound •  Design and evaluate many SVM classifiers •  Identify feature groups that recall all

concussed recordings with minimal false positives

Planning for on-device processing •  Goals: speed, accuracy, robustness •  Embedded speech/audio software (e.g.,

CMU Sphinx) for speech segmentation and feature extraction

•  Possible cloud service

mTBIs are a high profile problem in sports at all levels •  Common in contact sports (~300,000

per year) [1] •  Early (sideline) detection may help to

avoid important to avoid acute and chronic problems (Secondary Impact Syndrome, Parkinson’s disease, …)

mTBIs may affect speech •  Brain injuries affect coordination of

facial muscles used to articulate phonemes

•  Can cause slower reaction times and slurred speech [2]

The Project •  Collect multiple speech samples from

athletes (baseline, after practice, after hit, …) using a mobile app

•  Process speech signal to obtain acoustic features that can classify injury state

USING SPEECH AS A BIOMARKER TO DETECT MILD TRAUMATIC BRAIN INJURIES Nikhil Yadav, Christian Poellabauer, Patrick Flynn

Dept. of Computer Science & Engineering Advanced Diagnostics & Therapeutics Initiative

References:[1] Sports related recurrent brain injuries, MMWR MorbMortal Wkly Rep, vol. 6, pp. 224 - 227, 1997. [2] W. Ziegler and D.V. Cramon, Spastic dysarthria after acquired brain injury: An acoustic study, International Journal of Language & Communication Disorders, vol.21, no.2, pp. 173 - 187, 1986. Acknowledgements: This research is funded by the Advanced Diagnostics and Therapeutics Initiative at the University of Notre Dame and by the NFL/GE Head Health Challenge.

Recognized Word Start Time End TIme

“Sixth” 0.31 s 0.94 s

“I” 0.94 s 1.17 s

“Five” 1.17 s 1.95 s

“Eight” 1.95 s 2.23 s

“Seven” 2.23 s 2.74 s

“Four” 2.74 s 3.07 s

“Zero” 3.07 s 3.59 s