PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms

Abstract

Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.

A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram

“…We have considered U-Net 40 , PPG2IABP 12 , and V-Net 23 in this category. U-Net is the base architecture of several BP estimation approaches 22 , 41 , 42 . PPG2IABP 12 proposed GRU encoder-decoder architecture with an attention mechanism to estimate an ABP’s mean cycle.…”

“…As for the CNN-based models, U-Net consists of a contracting path and an expansive path with bypass connections in between to prevent the loss of border pixels in every convolution 40 . Several works 22,41,42,57 propose to use U-Net architecture to estimate ABP from PPG due to its capability of signal-to-signal translation. Similarly, Hill et al 23 proposed a V-Net architecture for the estimation of ABP.…”

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

“…Also, relatively smaller signal segments would reduce higher resource requirements during training. Considering these two points, the EEG and accelerometer signals were chopped into segments of 1024 sample points following the approach of the works presented in [77][78][79]. During segmenting the waveforms, 50% overlapping was carried out to increase the number of segments to twice.…”

“…(7)]. 'Zscore' normalization is important for normalizing signals of high variance [77], and 'range' normalization is utilized to constrain the amplitudes between 0 and 1, which is crucial for deep learning algorithms [77][78][79].…”

“…MAE is one of the primary evaluation metrics to calculate the construction error of the reconstructed signals through 1D-segmentation networks [77][78][79]…”