NABNet: A Nested Attention-guided BiConvLSTM network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG signals

Abstract

Backgroundand Motivations: Continuous Blood Pressure (BP) monitoring is crucial for real-time health tracking, especially for people with hypertension and cardiovascular diseases (CVDs). The current cuff-based BP monitoring methods are non-invasive but discontinuous while continuous BP monitoring methods are mostly invasive and can only be applied in a clinical setup to patients being monitored by advanced equipment and medical experts. Several studies have reported different techniques for predicting BP values from non-invasive Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals. Apart from BP readings, estimating ABP waveforms from non-invasive signals can provide vital body parameters such as Mean Arterial Pressure (MAP) which can be used to determine poor organ perfusion, nutrient supply to organs, and cardiovascular diseases (CVDs), etc. MethodsIt is challenging to estimate ABP waveforms while maintaining a high BP prediction performance and ABP waveform pattern. In this work, we propose a novel approach for ABP waveform estimation by separating the task into BP prediction and a normalized ABP waveform estimation through segmentation from PPG, PPG derivatives, and ECG signals, and combining afterward. We propose the Nested Attention-guided BiConvLSTM Network or NABNet which uses LSTM blocks during segmentation for better handling of the existing phase shifts between PPG, ECG, and ABP signals. Several experiments were performed to improve the ABP reconstruction performance, which was combined with an existing BP prediction pipeline for the non-invasive estimation of ABP waveforms. ResultsThe proposed framework can robustly estimate ABP waveforms from PPG and ECG signals by reaching a high MAP performance and low construction error while maintaining the overall Grade A performance of the BP prediction pipeline. ConclusionLinearly translating the range-normalized, synthesized ABP segments by corresponding SBP and DBP predictions from the BP prediction pipeline managed to robustly estimate ABP waveforms from PPG and ECG signals.