Reconstruction of ABP Waveform from the ECG or PPG Signals Using Enhanced 1-D UNetwork

ABP waveforms show how well a patient's blood responds to changes in arterial flow and can be used to analyze a patient's cardiovascular health, including heart rate analysis, stress analysis, and cardiovascular disease diagnosis. However, it is more difficult to obtain the ABP waveform than the ECG and PPG signals, and many studies are being conducted on reconstruction of the ABP waveform by recombination of bio-signals. Reconstruction of the ABP waveform has emerged as a major problem in signal distortion due to waveform destruction due to normalization/normalization of the input ABP waveform. In this study, we propose a method that can simultaneously output blood pressure estimation and ABP waveforms by inputting the raw ABP signal into the model as it is and using the U-net network coupled multi-task learning architecture model. Three signals (ECG, PPG, ECG-PPG) were trained as feature vectors through the U-net model and the results were compared. The results demonstrate the potential for replacing ECG signals with PPG signals and overcoming the current constraints of optimizing noninvasive reconstruction of ABP waveforms via U-net networks. In addition, we demonstrate that the U-net network combined multi-task learning architecture model can simultaneously perform blood pressure estimation and output of ABP waveforms without damaging the raw signal.