Deep Learning-Augmented Sleep Spindle Detection for Acute Disorders of Consciousness: Integrating CNN and Decision Tree Validation
Bibliographic record
Abstract
Sleep spindles, which are key biomarkers of non-rapid eye movement stage 2 sleep, play a crucial role in predicting outcomes for patients with acute disorders of consciousness (ADOC). However, several critical challenges remain in spindle detection: 1) the limited use of automated spindle detection in ADOC; 2) the difficulty in identifying low-frequency spindles in patient populations; and 3) the lack of effective tools for quantitatively analyzing the relationship between spindle density and patient outcomes. To address these challenges, we propose a novel Deep Learning-Augmented algorithm for automated sleep spindle detection in ADOC patients. This method combines Convolutional Neural Networks with decision tree-assisted validation, using wavelet transform principles to enhance detection accuracy and sensitivity, especially for the slow spindles commonly found in ADOC patients. Our approach not only demonstrates superior performance and reliability but also has the potential to significantly improve diagnostic precision and guide treatment strategies when integrated into clinical practice. Our algorithm was evaluated on the Montreal Archive of Sleep Studies - Session 2 (MASS SS2, n = 19), achieving average F1 scores of 0.798 and 0.841 compared to annotations from two experts. On a self-recorded dataset from ADOC patients (n = 24), it achieved an F1 score of 0.745 compared to expert annotations. Additionally, our analysis using the Spearman correlation coefficient revealed a moderate positive correlation between sleep spindle density and 28-day Glasgow Outcome Scale scores in ADOC patients. This suggests that spindle density could serve as a prognostic marker for predicting clinical outcomes and guiding personalized patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".