  Extensive research has been performed on continuous, non-invasive, cuffless
blood pressure (BP) measurement using artificial intelligence algorithms. This
approach involves extracting certain features from physiological signals like
ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from
Arterial Blood Pressure (ABP) signals as dependent variables, and then using
machine learning algorithms to develop a blood pressure estimation model based
on these data. The greatest challenge of this field is the insufficient
accuracy of estimation models. This paper proposes a novel blood pressure
estimation method with a clustering step for accuracy improvement. The proposed
method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR),
and Heart Rate (HR) features from Electrocardiogram (ECG) and
Photoplethysmogram (PPG) signals as the inputs of clustering and regression,
extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP)
features from ABP signals as dependent variables, and finally developing
regression models by applying Gradient Boosting Regression (GBR), Random Forest
Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster.
The method was implemented using the MIMICII dataset with the silhouette
criterion used to determine the optimal number of clusters. The results showed
that because of the inconsistency, high dispersion, and multi-trend behavior of
the extracted features vectors, the accuracy can be significantly improved by
running a clustering algorithm and then developing a regression model on each
cluster, and finally weighted averaging of the results based on the error of
each cluster. When implemented with 5 clusters and GBR, this approach yielded
an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were
significantly better than the best results without clustering (DBP: 6.27, SBP:
6.36).
