Abstract
High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.
Highlights
High blood pressure (BP) is one of the acute health concerns that may lead to hazardous health complications such as atherosclerosis, clots, heart attack, stroke, kidney diseases, and dementia [1]
Cuff-less BP monitoring can be implemented using a PPG device that is both non-invasive and allows for continuous patient monitoring without the use of an invasive line. While invasive lines such as the central venous line and arterial line remain the gold standard for cardio-vascular monitoring of cardiac critical care unit patients, the results of this paper present the possibility of accurate, lower cost, and less invasive means of detection beyond a patient’s intensive care units (ICUs) stay
This paper focused on the extraction of PPG signals to derive key features of invasive arterial BP
Summary
High blood pressure (BP) is one of the acute health concerns that may lead to hazardous health complications such as atherosclerosis, clots, heart attack, stroke, kidney diseases, and dementia [1]. It has been proven that automated machine learning (AutoML) is more effective and provides better performance Another direction in this field is estimating BP from PPG through an artificial neural network (ANN) [20,29], whereby the key features such as amplitudes and cardiac component phases have been extracted using a fast Fourier transformation (FFT). The authors in [47] addressed the issue of reducing accuracy due to the necessity of regular calibration in current models for BP estimation from PPG In their model [47], a deep recurrent neural network (DRNN) with long short-term memory (LSTM) is utilized to construct a model for the time-series BP data, whereby
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