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

Abstract

Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, 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 MIMIC-II data set with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multitrend 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).

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

“…The two studies both used manual check to determine the quality of the signal, which needs to be improved in the future. Similarly, after k means clustering with three features extracted from ECG and PPG signals, Farki et al [ 59 ] employed gradient boosting, random forest, and multilayer perceptron regression methods to regress the blood pressure for each cluster. The method was validated on the MIMIC dataset and yielded an MAE of 2.56 for SBP and 2.23 for DBP, but the details of the dataset were not clearly disclosed.…”

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring

“…In 2022, Gupta et al 113 investigated the prediction accuracy of RF and DT on the UCI and MIMIC I datasets and reported that RF outperformed other models. Farki et al 114 developed a clustering-based algorithm to elevate the performance of BP estimation using RF. In 2021, Ma et al 115 leveraged the information entropy of signals from wearable devices to train an RF model that had higher accuracy than LR or SVR.…”

A Novel Convolutional Neural Network Deep Learning Implementation for Cuffless Heart Rate and Blood Pressure Estimation

“…A novel clustering-based algorithm has been used recently to determine SBP and DPB from ABP by extracting only four features from ECG and PPG. They applied gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster of MIMIC II data [25]. In the review literature [26], the authors provide a recent comprehensive advancement for non-invasive cuff-less BP using the PPG or ECG.…”