Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data

Jason A. Thomas1‡, Hannah A. Burkhardt1‡, Safina Chaudhry1, Anthony D. Ngo1, Saransh Sharma1, Larry Zhang1, Rhoda Au2, Reza Hosseini Ghomi3

1University of Washington, Seattle, WA; 2Boston University, Boston, MA; 3UW Medicine, Seattle, WA; These Authors Contributed Equally

Abstract

BACKGROUND: There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention. OBJECTIVE: To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses. METHODS: This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association. RESULTS: Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names. CONCLUSION: Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.

Acoustic parameters

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) has the followings categories of variables (Eyben, 2016):

Frequency related parameters:

Energy/Amplitude related parameters:

Spectral (balance) parameters:

The extended Geneva Minimal Acoustic Parameter Set (eGeMAPS) includes additional features, including the following 6 temporal features:

Neuropsychological parameters

The FHS administers the following standard neuropsychological tests:

Taken from the Wechsler Memory Scale (WMS):


Taken from the Wechsler Adult Intelligence Scale (WAIS):


Others:

Interactive heatmap

This visualization is a heatmap of the associations between the various acoustic and neuropsychological variables.

The figure implements filtering and details on demand behavior. Click on a box, or shift-click on multiple boxes, to filter. Double click on a box to clear the filter. Click on a cell in order to view the scatter plot of the two variables. This allows on-demand investigation of the relationships between the variables. The default box selected shows a positive correlation between the acoustic variable 'jitter' (deviations in individual consecutive F0 period lengths) and time to complete a neuropsychological task (worse cognitive performance)


Interactive Heatmap allowing hover display of values, filtering, and details on demand via cell selection to then display (top) a scatterplot allowing further examination of the relationship between the two selected variables.

Acknowledgements & Disclosures & References

This study was supported in part by the National Library of Medicine (NLM) Training Grant T15LM007442.

Dr. Hosseini Ghomi’s work was supported by the VA Advanced Fellowship Program in Parkinson’s Disease.

Disclosures: Dr. Hosseini Ghomi is a stockholder of NeuroLex Laboratories.

Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., … Truong, K. P. (2016). The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing. IEEE Transactions on Affective Computing, 7(2), 190–202. https://doi.org/10.1109/TAFFC.2015.2457417

Spreen & Risser. (1998). Acquired Aphasia (Third Edition)

Maccow, G. (2011). WMS-IV: Administration, Scoring, Basic Interpretation. Pearson.

Heerema, E. (2019). Administration, Scoring, and Interpretation of the Trail Making Test. Available at https://www.verywellhealth.com/dementia-screening-tool-the-trail-making-test-98624

Springer Link. (2013). Finger-Tapping Test. Encyclopedia of Autism Spectrum Disorders - 2013 Edition. Available at https://link.springer.com/referenceworkentry/10.1007%2F978-1-4419-1698-3_343.

Mevius, J. (2019). Hooper Visual Organization Test (VOT). Elderly Driving Assessments. Available at http://elderlydrivingassessments.com/hooper.php