Features and Machine Learning Classification of Connected Speech Samples from Patients with Autopsy Proven Alzheimer's Disease with and without Additional Vascular Pathology
Issue title: 2013 International Congress on Vascular Dementia
Affiliations: [a] Neuroscience Research Centre, Cardiovascular and Cell Sciences Research Institute, St. George's, University of London, London, UK | [b] Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK | [c] Institute of Ageing in Africa, Division of Geriatric Medicine, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
Correspondence:
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Correspondence to: Dr. Peter Garrard, Neuroscience Research Centre, Cardiovascular and Cell Sciences Research Institute, St. George's University of London, Cranmer Terrace, London SW17 0RE, UK. Tel.: +44 208 725 5117; Fax: +44 208 725 2950; E-mail: pgarrard@sgul.ac.uk.
Abstract: Mixed vascular and Alzheimer-type dementia and pure Alzheimer's disease are both associated with changes in spoken language. These changes have, however, seldom been subjected to systematic comparison. In the present study, we analyzed language samples obtained during the course of a longitudinal clinical study from patients in whom one or other pathology was verified at post mortem. The aims of the study were twofold: first, to confirm the presence of differences in language produced by members of the two groups using quantitative methods of evaluation; and secondly to ascertain the most informative sources of variation between the groups. We adopted a computational approach to evaluate digitized transcripts of connected speech along a range of language-related dimensions. We then used machine learning text classification to assign the samples to one of the two pathological groups on the basis of these features. The classifiers' accuracies were tested using simple lexical features, syntactic features, and more complex statistical and information theory characteristics. Maximum accuracy was achieved when word occurrences and frequencies alone were used. Features based on syntactic and lexical complexity yielded lower discrimination scores, but all combinations of features showed significantly better performance than a baseline condition in which every transcript was assigned randomly to one of the two classes. The classification results illustrate the word content specific differences in the spoken language of the two groups. In addition, those with mixed pathology were found to exhibit a marked reduction in lexical variation and complexity compared to their pure AD counterparts.
