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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".