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

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).

Cuff-Less Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Based on VGG19-LSTM Network

In recent years, studies have found that the hierarchical neural network with LSTM network has higher accuracy than another feature engineering. Therefore, this paper first tries to build a multi-stage blood pressure estimation model through VGG19 and LSTM network. Based on the time node of the R wave peak in the QRS waveform in ECG, VGG19 is used to extract various higher-dimensional and rich life characteristics in the PPG signal segment by heartbeat as the unit and focus on processing the dynamics of SBP and DBP Correlation, finally use the LSTM model to extract the time dependence of the vital signs. Results: Experiments show that compared with similar multi-stage models, this model has higher accuracy. The performance of this method meets the Advancement of Medical Instrumentation (AAMI) standard and reaches the A level of the British Hypertension Society (BHS) standard. The average error and standard deviation of the estimated value of SBP were 1.7350 4.9606 mmHg, and the average error and standard deviation of the estimated value of DBP were 0.7839 2.7700 mmHg, respectively.