A Novel Clustering-Based Algorithm for Continuous and Noninvasive Cuff-Less Blood Pressure Estimation
| Autor: | Ali Farki, Reza Baradaran Kazemzadeh, Elham Akhondzadeh Noughabi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Medicine (General) Article Subject Computer Science - Artificial Intelligence Biomedical Engineering FOS: Physical sciences Blood Pressure Health Informatics Machine Learning (cs.LG) R5-920 Artificial Intelligence FOS: Electrical engineering electronic engineering information engineering Medical technology Cluster Analysis Humans Electrical Engineering and Systems Science - Signal Processing R855-855.5 Photoplethysmography Blood Pressure Determination Physics - Medical Physics Artificial Intelligence (cs.AI) Surgery Medical Physics (physics.med-ph) Algorithms Research Article Biotechnology |
| Zdroj: | Journal of Healthcare Engineering, Vol 2022 (2022) Journal of Healthcare Engineering |
| ISSN: | 2040-2309 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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