| --- |
| language: |
| - en |
| metrics: |
| - mae |
| - mse |
| - r_squared |
| library_name: keras |
| tags: |
| - encoder |
| - physionet |
| - afdb |
| - mit-bih |
| - biology |
| - cvd |
| - dnn |
| - ann |
| - keras |
| - tensorflow |
| - hdf5 |
| --- |
| |
| This model is a Transformer-based encoder for heart rate (HR) sequences, designed to learn robust representations of short-term HR fluctuations (HF components) in a self-supervised pretraining setup. |
|
|
| Key features: |
|
|
| - HR-only input: Sequence of heart rate measurements (BPM). |
| - Adaptive Normalization: Internal Normalization layer learns mean and variance from training HR data. |
| - Pre-LN Transformer: Multi-layer Pre-LayerNorm Transformer with residual connections for stable sequence modeling. |
| - Masked pretraining: Randomly masks portions of the HR sequence during training to learn contextual representations. |
| - Robust to short-term HR spikes: Designed to handle physiological or situational changes (e.g., exercise, stress, sudden excitement). |
|
|
| ## Intended Use |
| - Pretraining for downstream HR/HRV tasks, such as: |
| - Heart rate prediction / imputation |
| - Wearable biosignal modeling |
| - Works on fixed-length HR windows, e.g., 128-minute sequences. |
|
|
| ## Training Data |
| - Derived from the AFDB (Atrial Fibrillation Database) ECG recordings. |
| - HR sequences extracted via fast R-peak detection and sliding-window HR computation. |
| - Masking ratio: 0.05 (configurable during training). |
|
|
| ## Evaluation Metrics |
| - Masked sequence reconstruction evaluated via: |
| - Mean Absolute Error (MAE) |
| - Root Mean Squared Error (RMSE) |
| - R² (variance explained) |
|
|
| ## AFDB Citation |
| Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. RRID:SCR_007345. |
