Background

In this study, we will be using 3-lead ECG electrodes to collect ECG Signals. This approach simplifies the process of identifying the correct waves and eliminates the need for precise placement in specific locations. Our main objective is to clean the ECG data collected by the hardware team which will be helpful in the feature extraction to accurately estimate the BP. The proposed system consists of the following steps: 1. The ECG Signal data: We will collect and store the ECG Signal data, which will serve as the fundamental inputs for our model. 2. Preprocessing the ECG Signal: The ECG Signal will undergo preprocessing to ensure they are suitable for model training, validation, and testing. 3. We will create a Deep Neural Network (DNN) model that can denoise the ECG Signal and help in extracting useful features from the preprocessed signals during the BP Estimation. This model will be trained to estimate denoised signals concurrently. 4. Calibration-free model training, validation, and testing: We will train, validate, and test the model without the need for calibration, making the process more efficient and user-friendly.