DeepArousal-Net: A Multi-Block Recurrent Deep Learning Model for Proactive Forecasting of Non-Apneic Arousals From Multichannel PSG
Bibliographic record
Abstract
OBJECTIVE: This study aimed to develop a deep learning model capable of accurately forecasting non-apneic sleep arousals, which are brief awakenings that disrupt sleep continuity and contribute to daytime fatigue. METHODS: We introduce DeepArousal-Net, a novel deep learning model designed to predict non-apnea arousals using multichannel polysomnography (PSG) records. DeepArousal-Net employs a multi-block architecture composed of convolutional neural networks (CNNs) to extract features from a comprehensive set of PSG signals, including EEG, ECG, EOG, EMG, oxygen saturation, and airflow. Bidirectional Long Short-Term Memory (Bi-LSTM) layers are incorporated to capture temporal dependencies in the extracted features. RESULTS: DeepArousal-Net achieved an accuracy of 81.31%, sensitivity of 71.23%, and specificity of 81.90% in forecasting arousals 30 seconds in advance. The model demonstrated superior performance compared to traditional time-series prediction methods. CONCLUSION: DeepArousal-Net's ability to forecast non-apneic sleep arousals marks a significant advancement over existing post-event detection systems. SIGNIFICANCE: By anticipating arousals, DeepArousal-Net opens new possibilities for the development of innovative interventions and personalized sleep management strategies, potentially leading to improved sleep quality and overall well-being.
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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) | 0.001 | 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 | 0.001 |
| 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".