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Extensive research has been performed on continuous, non-invasive, cufflessblood pressure (BP) measurement using artificial intelligence algorithms. Thisapproach involves extracting certain features from physiological signals likeECG, PPG, ICG, BCG, etc. as independent variables and extracting features fromArterial Blood Pressure (ABP) signals as dependent variables, and then usingmachine learning algorithms to develop a blood pressure estimation model basedon these data. The greatest challenge of this field is the insufficientaccuracy of estimation models. This paper proposes a novel blood pressureestimation method with a clustering step for accuracy improvement. The proposedmethod involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR),and Heart Rate (HR) features from Electrocardiogram (ECG) andPhotoplethysmogram (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 developingregression models by applying Gradient Boosting Regression (GBR), Random ForestRegression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster.The method was implemented using the MIMICII dataset with the silhouettecriterion used to determine the optimal number of clusters. The results showedthat because of the inconsistency, high dispersion, and multi-trend behavior ofthe extracted features vectors, the accuracy can be significantly improved byrunning a clustering algorithm and then developing a regression model on eachcluster, and finally weighted averaging of the results based on the error ofeach cluster. When implemented with 5 clusters and GBR, this approach yieldedan MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which weresignificantly better than the best results without clustering (DBP: 6.27, SBP:6.36).

A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation