Cuff-less Arterial Blood Pressure Waveform Synthesis from Single-site
  PPG using Transformer & Frequency-domain Learning
Muhammad Ahmad Tahir Ahsan Mehmood Muhammad Mahboob Ur Rahman Muhammad Wasim Nawaz Kashif Riaz Qammer H. Abbasi
We propose two novel purpose-built deep learning (DL) models for synthesis of
the arterial blood pressure (ABP) waveform in a cuff-less manner, using a
single-site photoplethysmography (PPG) signal. We utilize the public UCI
dataset on cuff-less blood pressure (CLBP) estimation to train and evaluate our
DL models. Firstly, we implement a transformer model that incorporates
positional encoding, multi-head attention, layer normalization, and dropout
techniques, and synthesizes the ABP waveform with a mean absolute error (MAE)
of 14. Secondly, we implement a frequency-domain (FD) learning approach where
we first obtain the discrete cosine transform (DCT) coefficients of the PPG and
ABP signals corresponding to two cardiac cycles, and then learn a
linear/non-linear (L/NL) regression between them. We learn that the FD L/NL
regression model outperforms the transformer model by achieving an MAE of 11.87
and 8.01, for diastolic blood pressure (DBP) and systolic blood pressure (SBP),
respectively. Our FD L/NL regression model also fulfills the AAMI criterion of
utilizing data from more than 85 subjects, and achieves grade B by the BHS
criterion.