A CNN - ELM-Based Method for Ballistocardiogram Classification in a Clinical Environment
Bibliographic record
Abstract
The ballistocardiogram (BCG) represents rich signals that have been adopted in various clinical applications. Still, vital signs detection via BCG is a troublesome task because the BCG waveform morphology depends on the measurement device. Additionally, BCG can be different between and within-subjects, hence in this paper, we applied a deep learning-based approach, namely convolutional neural network (CNN) and extreme learning machine (ELM), to discriminate between BCG and non-BCG signals. BCG signals were acquired with an IoT -based microbend fiber optic sensor mat from ten patients diagnosed with obstructive sleep apnea and underwent drug-induced sleep endoscopy. Three methods, including undersampling, oversampling, and generative adversarial networks (GANs), were used to balance the number of BCG vs. non-BCG signals. Furthermore, the system performance was assessed using 10-fold cross-validation. Overall, the best results were achieved using the CNN-ELM with GANs as a data balancing method. The average accuracy, precision, recall F -score were 94%, 90%, 98%, and 94%, respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".