Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- mae
+- mse
+- r_squared
+library_name: keras
+---
+
+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)
+
+### Example validation performance:
+
+MAE ≈ 1.45 BPM
+RMSE ≈ 3.59 BPM
+R² ≈ 0.86
+Limitations
+Model trained on AFDB dataset; generalization to other populations or devices may require fine-tuning.
+Designed for resting or moderate activity HR; extreme exercise scenarios may require additional smoothing or adaptation.
+
+## 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.