Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort
Aim: Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA &...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Open Exploration Publishing Inc.
2021-06-01
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| Series: | Exploration of Medicine |
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| Online Access: | https://www.explorationpub.com/Journals/em/Article/100144 |
| _version_ | 1819141152283033600 |
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| author | Larry Zhang Anthony Ngo Jason A. Thomas Hannah A. Burkhardt Carolyn M. Parsey Rhoda Au Reza Hosseini Ghomi |
| author_facet | Larry Zhang Anthony Ngo Jason A. Thomas Hannah A. Burkhardt Carolyn M. Parsey Rhoda Au Reza Hosseini Ghomi |
| author_sort | Larry Zhang |
| collection | DOAJ |
| description | Aim: Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905–2) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features.
Methods: Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants’ Mini-Mental State Examination (MMSE) scores.
Results: Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869].
Conclusions: Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment. |
| first_indexed | 2024-12-22T11:49:54Z |
| format | Article |
| id | doaj.art-b811faa7c7564ba99c3349a2663de5b3 |
| institution | Directory Open Access Journal |
| issn | 2692-3106 |
| language | English |
| last_indexed | 2024-12-22T11:49:54Z |
| publishDate | 2021-06-01 |
| publisher | Open Exploration Publishing Inc. |
| record_format | Article |
| series | Exploration of Medicine |
| spelling | doaj.art-b811faa7c7564ba99c3349a2663de5b32022-12-21T18:27:01ZengOpen Exploration Publishing Inc.Exploration of Medicine2692-31062021-06-012323225210.37349/emed.2021.00044Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging CohortLarry Zhang0https://orcid.org/0000-0001-5573-1626Anthony Ngo1https://orcid.org/0000-0001-9271-1130Jason A. Thomas2https://orcid.org/0000-0003-3892-7197Hannah A. Burkhardt3https://orcid.org/0000-0002-9386-5569Carolyn M. Parsey4https://orcid.org/0000-0002-8555-6512Rhoda Au5https://orcid.org/0000-0001-7742-4491Reza Hosseini Ghomi6https://orcid.org/0000-0003-4369-8237Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, Indiana 47408, United States; Department of Informatics, Indiana University Bloomington, Bloomington, Indiana 47408, United StatesDepartment of Statistics, University of Washington, Seattle, Washington 98195-0005, United StatesDepartment of Biomedical Informatics and Medical Education, University of Washington Seattle Campus, Seattle, Washington 98195-0005, United StatesDepartment of Biomedical Informatics and Medical Education, University of Washington Seattle Campus, Seattle, Washington 98195-0005, United StatesDepartment of Neurology, University of Washington, Seattle, Washington 98195-0005, United StatesDepartment of Anatomy and Neurobiology, Neurology, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts 02118, United StatesDepartment of Neurology, University of Washington, Seattle, Washington 98195-0005, United StatesAim: Although clinicians primarily diagnose dementia based on a combination of metrics such as medical history and formal neuropsychological tests, recent work using linguistic analysis of narrative speech to identify dementia has shown promising results. We aim to build upon research by Thomas JA & Burkardt HA et al. (J Alzheimers Dis. 2020;76:905–2) and Alhanai et al. (arXiv:1710.07551v1. 2020) on the Framingham Heart Study (FHS) Cognitive Aging Cohort by 1) demonstrating the predictive capability of linguistic analysis in differentiating cognitively normal from cognitively impaired participants and 2) comparing the performance of the original linguistic features with the performance of expanded features. Methods: Data were derived from a subset of the FHS Cognitive Aging Cohort. We analyzed a sub-selection of 98 participants, which provided 127 unique audio files and clinical observations (n = 127, female = 47%, cognitively impaired = 43%). We built on previous work which extracted original linguistic features from transcribed audio files by extracting expanded features. We used both feature sets to train logistic regression classifiers to distinguish cognitively normal from cognitively impaired participants and compared the predictive power of the original and expanded linguistic feature sets, and participants’ Mini-Mental State Examination (MMSE) scores. Results: Based on the area under the receiver-operator characteristic curve (AUC) of the models, both the original (AUC = 0.882) and expanded (AUC = 0.883) feature sets outperformed MMSE (AUC = 0.870) in classifying cognitively impaired and cognitively normal participants. Although the original and expanded feature sets had similar AUC, the expanded feature set showed better positive and negative predictive value [expanded: positive predictive value (PPV) = 0.738, negative predictive value (NPV) = 0.889; original: PPV = 0.701, NPV = 0.869]. Conclusions: Linguistic analysis has been shown to be a potentially powerful tool for clinical use in classifying cognitive impairment. This study expands the work of several others, but further studies into the plausibility of speech analysis in clinical use are vital to ensure the validity of speech analysis for clinical classification of cognitive impairment.https://www.explorationpub.com/Journals/em/Article/100144clock drawing testperioperative neuropsychologytranscatheter aortic valve replacementhospital outcomes |
| spellingShingle | Larry Zhang Anthony Ngo Jason A. Thomas Hannah A. Burkhardt Carolyn M. Parsey Rhoda Au Reza Hosseini Ghomi Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort Exploration of Medicine clock drawing test perioperative neuropsychology transcatheter aortic valve replacement hospital outcomes |
| title | Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort |
| title_full | Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort |
| title_fullStr | Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort |
| title_full_unstemmed | Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort |
| title_short | Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort |
| title_sort | neuropsychological test validation of speech markers of cognitive impairment in the framingham cognitive aging cohort |
| topic | clock drawing test perioperative neuropsychology transcatheter aortic valve replacement hospital outcomes |
| url | https://www.explorationpub.com/Journals/em/Article/100144 |
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