Sleep Apnea Detection From Variational Mode Decomposed EEG Signal Using a Hybrid CNN-BiLSTM
Bibliographic record
Abstract
Sleep apnea, a severe sleep disorder, is a clinically complicated disease that requires timely diagnosis for proper treatment. In this paper, an automated deep learning-based approach is proposed for the detection of sleep apnea frames from electroencephalogram (EEG) signals. Unlike conventional methods of direct feature extraction from EEG signals, the variational mode decomposition (VMD) algorithm is utilized in the proposed method to decompose the EEG signals into a number of modes. Use of such decomposed EEG signals for feature extraction offers efficient processing of the variations introduced in the frequency spectrum during apnea events irrespective of particular patients. Afterward, a fully convolutional neural network (FCNN) is proposed to separately extract the temporal features from each VMD mode in parallel while maintaining their temporal dependencies. The FCNN block utilizes causal dilated convolutions with increasing dilation rates along with multiple kernel operations in convolutions. Subsequently, for further exploration of the inter-modal temporal variations, these extracted features from different EEG-modes are jointly optimized with a stack of bi-directional long short term memory (LSTM) layers. Hence, the trained and optimized network is capable of generating predictions of apnea frames during the evaluation phase. Contrary to other studies, this study is carried out in a subject independent manner where separate subjects are considered for training and testing. Additionally, a semi-supervised approach is explored where for facilitating better classification performance on a subject's frames, a small portion of the patient's data is included in training to leverage insight regarding the possible environmental variations. Extensive experimentations on three publicly available datasets provide average accuracy of 93.22%, 93.25% and 89.41% in the subject-independent cross-validation scheme.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".