Cuffless Blood Pressure Estimation Using Multi-modal Bio-signals

This is part of Ms. XIANG Ting’s PhD program at City University, where we employ a variety of physiological signals, including PPG, ECG, IPG, and temperature, to estimate blood pressure using both machine learning and mathematical models. The data for all subjects was obtained from COCHE Lab, and my contributions to this project encompass the following tasks:

1. Data Preprocessing: I handled the preprocessing of the multi-modal data, ensuring its suitability for analysis.
2. Feature Extraction and Normalization: I extracted relevant features from the data and normalized them for consistency.
3. Machine learning models: I employed a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model to extract features and estimate blood pressure.
4. Results from Multiple Input Signals: I fed various signal combinations and individual signals into the model, and the outcomes are as follows: