Project on blood pressure estimation from ECG and PPG signals.
This data set contains PPG recordings from 56 subjects who were not hospitalized during data collection. This dataset is intended to support the development of approaches for blood pressure estimation by analyzing PPG signals.
Blood Pressure Estimation from PPG Signals Using Machine Learning
Blood Pressure Estimation using PPG Signal and Demographic Features
Spiking ResNet‑18 (snnTorch) for blood‑pressure prediction from PPG. Implements PA‑B residual connections and Densely Additive Connections (Li et al., Rethinking residual connection in training large-scale spiking neural networks). Includes preprocessing for the bp‑benchmark dataset (González et al.).
Official reproducibility code and aggregate results for SAQM-MedFuse, a safety-aware quality-driven multimodal framework for cuffless blood pressure estimation and risk stratification from PPG and ECG.
Physiology-guided delay-banded cross-attention for subject-independent cuffless blood pressure estimation from ECG and PPG.
A PyTorch-based deep learning toolbox for Remote Photoplethysmography (rPPG) and continuous PPG-to-ABP (Arterial Blood Pressure) waveform estimation.
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