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Detecting cognitive impairment by eye movement analysis using automatic classification algorithms
Lagun D, Manzanares C, Zola SM, Buffalo EA, Agichtein E.. Journal of neuroscience methods, 2011-07-27
Topic: Risk, prevention and care in population studies
The Visual Paired Comparison (VPC) task is a recognition memory test that has shown promise for the detection of memory impairments associated with mild cognitive impairment (MCI). Because patients with MCI often progress to Alzheimer's Disease (AD), the VPC may be useful in predicting the onset of AD. VPC uses noninvasive eye tracking to identify how subjects view novel and repeated visual stimuli. Healthy control subjects demonstrate memory for the repeated stimuli by spending more time looking at the novel images, i.e., novelty preference. Here, we report an application of machine learning methods from computer science to improve the accuracy of detecting MCI by modeling eye movement characteristics such as fixations, saccades, and re-fixations during the VPC task. These characteristics are represented as features provided to automatic classification algorithms such as Support Vector Machines (SVMs). Using the SVM classification algorithm, in tandem with modeling the patterns of fixations, saccade orientation, and regression patterns, our algorithm was able to automatically distinguish age-matched normal control subjects from MCI subjects with 87% accuracy, 97% sensitivity and 77% specificity, compared to the best available classification performance of 67% accuracy, 60% sensitivity, and 73% specificity when using only the novelty preference information. These results demonstrate the effectiveness of applying machine-learning techniques to the detection of MCI, and suggest a promising approach for detection of cognitive impairments associated with other disorders.
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APA 7th
Lagun, D., Manzanares, C., Zola, S. M., Buffalo, E. A., & Agichtein, E. (2011). Detecting cognitive impairment by eye movement analysis using automatic classification algorithms. Journal of neuroscience methods. https://doi.org/10.1016/j.jneumeth.2011.06.027
Vancouver
Lagun D, Manzanares C, Zola SM, Buffalo EA, Agichtein E. Detecting cognitive impairment by eye movement analysis using automatic classification algorithms. Journal of neuroscience methods. 2011. doi:10.1016/j.jneumeth.2011.06.027. PMID: 21801750.
BibTeX
@article{lagun2011-21801750,
author = {Lagun, D. and Manzanares, C. and Zola, S. M. and Buffalo, E. A. and Agichtein, E.},
title = {{Detecting cognitive impairment by eye movement analysis using automatic classification algorithms}},
journal = {Journal of neuroscience methods},
year = {2011},
doi = {10.1016/j.jneumeth.2011.06.027},
pmid = {21801750},
url = {https://doi.org/10.1016/j.jneumeth.2011.06.027}
}
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Key findings
- The Visual Paired Comparison (VPC) task is a recognition memory test that has shown promise for the detection of memory impairments associated with mild cognitive impairment (MCI).
- Using the SVM classification algorithm, in tandem with modeling the patterns of fixations, saccade orientation, and regression patterns, our algorithm was able to automatically distinguish age-matched normal control subjects from MCI subjects with 87% accuracy, 97% sensitivity and 77% specificity, compared to the best available classification performance of 67% accuracy, 60% sensitivity, and 73% specificity when using only the novelty preference information.
- These results demonstrate the effectiveness of applying machine-learning techniques to the detection of MCI, and suggest a promising approach for detection of cognitive impairments associated with other disorders.
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Study details
- Evidence strengthLow
How this study scored 62/100
Published in a peer-reviewed journal, and it is a randomised controlled trial. Scored zero for sample size and effect reported because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 36 of the 58 points we could judge it on, which is 62 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of neuroscience methods) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 11 / 12 | A randomised controlled trial (from PubMed type “Randomized Controlled Trial”) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2011 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (pubmed indexes it as a clinical or observational study) |
| Randomised / blinded | 1 / 2 | Randomised (as stated in the record) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 21801750) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 36 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 36 of the 58 points we could judge it on is 62 out of 100. |
| Total | 62 / 100 | — |
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