Automatic early detection of cognitive decline
Bibliographic record
Abstract
Abstract Background While it is agreed that cognitive decline is underdiagnosed, there is still no single universally accepted and affordable screening method that satisfies all needs in the detection of cognitive impairment. The goal of this presentation is to demonstrate an automatic cognitive decline assessment with Neurosteer Aurora, utilizing a novel EEG analysis as a method for characterization of cognitive activity and detection of early cognitive impairment. Method Neurosteer has developed a miniature wearable neurological sensor combined with an automatic assessment tool for a wide range of brain monitoring and treatment needs. It is based on a single EEG channel and relies on advanced mathematical analysis for a decomposition of the EEG signal into multiple components. These components are then processed with machine learning algorithms to create high level biomarkers for real‐time brain activity interpretation. Additionally, Neurosteer has developed a fully automatic assessment and biomarkers for cognitive decline. It is based on a verbal and musical cognitive assessment during brain activity recording. Machine learning algorithms analyze the combined data to produce a simple‐to‐read report indicating the level of different aspects of cognitive decline. We applied this technology on three different healthy population (N=14, 40, 215) and cognitively impaired (N=24, 10, 9, 8) undergoing several automatically administered cognitive tasks. Result A few biomarkers were extracted using machine learning tools on the collected data. The biomarkers distinguished between different levels of cognitive load as well as resting state patterns. The new groups of cognitively impaired populations were examined with Neurosteer Aurora, completing short and automatic auditory assessments. The patterns of the predefined biomarkers were significantly different for each population, differentiating between clinical populations and negatively correlated with the severity of cognitive decline. Conclusion Using the novel brain activity interpretation and biomarkers enables an automatic and easy‐to‐administer cognitive decline assessment in the clinical population. Such assessment is a step forward towards a wide‐spread screening, allowing early and personalized intervention to slow down cognitive decline, improving health and quality of life outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".