Automatic detection and rating of dementia of alzheimer type through lexical analysis of spontaneous speech

Current methods of assessing dementia of Alzheimer type (DAT) in older adults involve structured interviews that attempt to capture the complex nature of deficits suffered. One of the most significant areas affected by the disease is the capacity for functional communication as linguistic skills break down. These methods often do note capture the true nature of language deficits in spontaneous speech. We address this issue by exploring novel automatic and objective methods for diagnosing patients through analysis of spontaneous speech. We detail several lexical approaches to the problem of detecting and rating DAT. The approaches explored rely on character n-gram-based techniques, shown recently to perform successfully in a different, but related task of automatic authorship attribution. We also explore the correlation of usage frequency of different parts of speech and DAT. We achieve a high 95% accuracy of detecting dementia when compared with a control group, and we achieve 70% accuracy in rating dementia in two classes, and 50% accuracy in rating dementia into four classes. Our results show that purely computational solutions offer a viable alternative to standard approaches to diagnosing the level of impairment in patients. These results are significant step forward toward automatic and objective means to identifying early symptoms of DAT in older adults.