Hybrid Optimized Techniques for EEG Based Mild Cognitive Impairment Detection Using Time Domain Feature Extraction
Bibliographic record
Abstract
Mild Cognitive Impairment (MCI) represents early cognitive changes that can signal the potential development of more serious memory and thinking problems.The EEG data is preprocessed by applying band pass filter and segmenting it into epochs of 5 secs.Subsequently, time domain feature extraction techniques including Kurtosis, Zero Crossing Rate (ZCR) and Hjorth parameters are explored and applied to the EEG signals.The investigation includes the integration of these techniques with 1D deep learning techniques like Convolutional Neural Networks (CNN) and Convolutional Recurrent Neural Networks (CRNN) and hybrid 1D deep optimized models like PBCNN (Population Based CNN) and PBCRNN (Population-Based CRNN).The impact of feature extraction on MCI detection accuracy is evaluated by comparing the results obtained with and without feature extraction.Additionally, the influence of epoch duration, considering 5-second epochs with 1 second overlap, is examined to determine the optimal duration for precise MCI classification using EEG data.The findings contribute to advancing the understanding of EEG data analysis techniques for early MCI detection.The proposed methods have significant clinical application value in the early screening and diagnosis of Mild Cognitive Impairment (MCI).Among the proposed models 1DPBCRNN works well with 90.01 accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".