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 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) | 0.000 | 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".