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Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks
Themistocleous C, Eckerström M, Kokkinakis D.. Frontiers in neurology, 2018-11-15
Topic: Risk, prevention and care in population studies
While people with mild cognitive impairment (MCI) portray noticeably incipient memory difficulty in remembering events and situations along with problems in decision making, planning, and finding their way in familiar environments, detailed neuropsychological assessments also indicate deficits in language performance. To this day, there is no cure for dementia but early-stage treatment can delay the progression of MCI; thus, the development of valid tools for identifying early cognitive changes is of great importance. In this study, we provide an automated machine learning method, using Deep Neural Network Architectures, that aims to identify MCI. Speech materials were obtained using a reading task during evaluation sessions, as part of the Gothenburg MCI research study. Measures of vowel duration, vowel formants ( F 1 to F 5), and fundamental frequency were calculated from speech signals. To learn the acoustic characteristics associated with MCI vs. healthy controls, we have trained and evaluated ten Deep Neural Network Architectures and measured how accurately they can diagnose participants that are unknown to the model. We evaluated the models using two evaluation tasks: a 5-fold crossvalidation and by splitting the data into 90% training and 10% evaluation set. The findings suggest first, that the acoustic features provide significant information for the identification of MCI; second, the best Deep Neural Network Architectures can classify MCI and healthy controls with high classification accuracy ( M = 83%); and third, the model has the potential to offer higher accuracy than 84% if trained with more data (cf., SD ≈15%). The Deep Neural Network Architecture proposed here constitutes a method that contributes to the early diagnosis of cognitive decline, quantify the progression of the condition, and enable suitable therapeutics.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Themistocleous, C., Eckerström, M., & Kokkinakis, D. (2018). Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks. Frontiers in neurology. https://doi.org/10.3389/fneur.2018.00975
Vancouver
Themistocleous C, Eckerström M, Kokkinakis D. Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks. Frontiers in neurology. 2018. doi:10.3389/fneur.2018.00975. PMID: 30498472.
BibTeX
@article{themistocleous2018-30498472,
author = {Themistocleous, C. and Eckerström, M. and Kokkinakis, D.},
title = {{Identification of Mild Cognitive Impairment From Speech in Swedish Using Deep Sequential Neural Networks}},
journal = {Frontiers in neurology},
year = {2018},
doi = {10.3389/fneur.2018.00975},
pmid = {30498472},
url = {https://doi.org/10.3389/fneur.2018.00975}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- To learn the acoustic characteristics associated with MCI vs. healthy controls, we have trained and evaluated ten Deep Neural Network Architectures and measured how accurately they can diagnose participants that are unknown to the model.
- The findings suggest first, that the acoustic features provide significant information for the identification of MCI; second, the best Deep Neural Network Architectures can classify MCI and healthy controls with high classification accuracy ( M = 83%); and third, the model has the potential to offer higher accuracy than 84% if trained with more data (cf., SD ≈15%).
Why it matters
This study used deep neural networks on speech features to identify mild cognitive impairment, with the best models classifying MCI and healthy controls at about 83% accuracy. It is a diagnostic method and does not report any treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 42/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in neurology) |
| Journal's citation record | 2 / 4 | Frontiers in Neurology: journal h-index 162 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| 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 | 2 / 5 | 2018 — within 10 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 | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 73 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.3 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 3 / 6 | Cited 3.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 31 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Dimitrios Kokkinakis: h-index 15 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Johns Hopkins University: institution h-index 2,306 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30498472) |
| Free to read | 2 / 2 | Free to read |
| Total | 42 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fneur.2018.00975
- PubMed: 30498472
- Original source →
- Free full text: Frontiers in Neurology →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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