Enhanced balancing with integrated resampling cascade and advanced analysis of ‘seizureDetect’ dataset key features
Bibliographic record
Abstract
Accurate detection of epileptic seizures is crucial for effective patient care. This paper introduces ‘SeizureDetect’, a meticulously crafted dataset aimed at addressing class imbalance in epileptic and non-epileptic seizure activity. Our approach, known as Integrated Resampling Cascade (IRC), combines Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE, EasyEnsemble, and BalanceCascade techniques in order to balance dataset with 9,200 records per class. It ensures the integrity and diversity of the resampled data, with a particular emphasis on enhancing time series data handling using Borderline-SMOTE. Comprehensive analysis, including correlation examination, handling of missing values, and feature importance determination using a Random Forest classifier, enriches our understanding of dataset characteristics and feature relationships. Additionally, boxplot analysis and statistical examination of quartiles and whiskers are conducted to deepen insights into feature distributions. Furthermore, Individual Conditional Expectation (ICE) plots are utilized to visualize the impact of feature values on seizure detection. The current proposed methodology contributes to advancing epileptic seizure detection research, promising improved patient care and management.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".