Integration of Ballistocardiogram with PPG and ECG Using a CNN-LSTM Model for Cuff-Less Blood Pressure Estimation
Bibliographic record
Abstract
Recent advancements in wearable technology have revolutionized the capability for continuous and non-invasive monitoring of vital signs, such as blood pressure. This paper presents a novel approach leveraging a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to predict systolic and diastolic blood pressure (SBP and DBP) using data from wearable devices. Our model employs Photoplethysmography (PPG), Electrocardiography (ECG), and Ballistocardiography (BCG) signals, exploring their efficacy across different signal window sizes to optimize prediction accuracy. Through rigorous preprocessing, including advanced filtering and normalization techniques, we ensured data integrity for effective feature extraction. The model was evaluated against the rigorous standards set by IEEE and the Association for the Advancement of Medical Instrumentation (AAMI), demonstrating promising capability. These findings suggest the model's potential for integration into clinical workflows, offering a reliable alternative to traditional blood pressure monitoring methods. Further validation on a larger scale to confirm its efficacy and adaptability in diverse clinical scenarios is ongoing.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".