Automatic detection and rating of dementia of Alzheimer type through lexical analysis of spontaneous speech
Abstract
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.
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Out of 51 reviewed papers, only seven did three-way or four-way classification.
“Out of 51 reviewed papers, only seven did attempt three-way [50, 62–64] or four-way [12, 65, 66] classification.”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [66]
Seven reviewed papers are concerned with three or four disease stages.
“Outside of those, seven papers are concerned with three or four disease stages [12, 50, 62–66], two explore longitudinal cognitive changes (although still through binary classification) [67, 68], and one describes discourse patterns [69].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [66]
Thomas et al. reported 50% accuracy for a 4-way classifier on four groups of MMSE scores.
“Thomas et al. [66], also 4-way, only 50%, on four groups of MMSE scores.”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [66]
Only three articles were labeled as low replicability, lacking detail in at least two of the assessed sections, frequently data information and feature generation procedures.
“Only three articles were labeled as low replicability [66, 74, 85], as they lacked detail in at least two of those sections (frequently data information and feature generation procedures).”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [66]
Cross-validation is reported in all reviewed papers but five.
“Cross-validation (CV) is probably the most established practice for classifier evaluation. It is reported in all papers but five, of which two are not very recent [66, 93], another two do not report CV but report using a hold-out set [82, 91], and only one reports using neither CV nor a hold-out set procedure [95].”
From Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review (2020) · cites this paper as [66]
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- Authors
- Thomas, C., Keselj, V., Cercone, N., Rockwood, K., & Asp, E.
- Affiliations
- Dalhousie University (CA)
- St. Mary's University (CA)
- ORCID
- Rockwood, K.
- Venue
- IEEE International Conference Mechatronics and Automation, 2005
- Publisher
- IEEE
- Published
- 2006-08-15
- Volume
- 3
- Pages
- 1569-1574
- OpenAlex
- W2151969795
- Cited by
- 110
- References
- 24
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 9 · 2022: 8 · 2021: 11 · 2020: 10 · 2019: 7 · 2018: 14 · 2017: 9 · 2016: 13 · 2015: 6 · 2014: 7 · 2013: 3
- FWCI
- 14.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Mechatronics and Automation (publishedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, dementia detection, spontaneous speech, lexical analysis, character n-grams, automated diagnosis, older adults, language deficits
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