Linguistic Features Identify Alzheimer's Disease in Narrative Speech.
BACKGROUND
Although memory impairment is the main symptom of Alzheimer's disease (AD), language impairment can be an important marker. Relatively few studies of language in AD quantify the impairments in connected speech using computational techniques.


OBJECTIVE
We aim to demonstrate state-of-the-art accuracy in automatically identifying Alzheimer's disease from short narrative samples elicited with a picture description task, and to uncover the salient linguistic factors with a statistical factor analysis.


METHODS
Data are derived from the DementiaBank corpus, from which 167 patients diagnosed with "possible" or "probable" AD provide 240 narrative samples, and 97 controls provide an additional 233. We compute a number of linguistic variables from the transcripts, and acoustic variables from the associated audio files, and use these variables to train a machine learning classifier to distinguish between participants with AD and healthy controls. To examine the degree of heterogeneity of linguistic impairments in AD, we follow an exploratory factor analysis on these measures of speech and language with an oblique promax rotation, and provide interpretation for the resulting factors.


RESULTS
We obtain state-of-the-art classification accuracies of over 81% in distinguishing individuals with AD from those without based on short samples of their language on a picture description task. Four clear factors emerge: semantic impairment, acoustic abnormality, syntactic impairment, and information impairment.


CONCLUSIONS
Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.
Department of Computer Science, University of Toronto, Toronto, Canada.
Kathleen C
KC
Fraser
Rotman Research Institute, Toronto, Canada.
Jed A
JA
Meltzer
Department of Computer Science, University of Toronto, Toronto, Canada.|Toronto Rehabilitation Institute-UHN, Toronto, Canada.
Frank
F
Rudzicz
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000150:complications* / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D007806:Language Disorders / Q000209:etiology*; D008037:Linguistics; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D033262:Narration; D010775:Photic Stimulation; D013060:Speech / Q000502:physiology*; D014705:Verbal Behavior
Automatic data processing; factor analysis; geriatric assessment; heterogeneity; language; statistical