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Öğe A non-invasive continuous cuffless blood pressure estimation using dynamic Recurrent Neural Networks(Elsevier Sci Ltd, 2020) Senturk, Umit; Polat, Kemal; Yucedag, IbrahimCardiovascular diseases (CVD) have become the most important health problem of our time. High blood pressure, which is cardiovascular disease, is a risk factor for death, stroke, and heart attack. Blood pressure measurement is commonly used to limit blood flow in the arm or wrist, with the cuff. Since blood pressure cannot be measured continuously in this method, the dynamics underlying blood pressure cannot be determined and are inefficient in capturing symptoms. This paper aims to perform blood pressure estimation using Photoplethysmography (PPG) and Electrocardiography (ECG) signals that do not obstruct the vascular access. These signals were filtered and segmented synchronously from the R interval of the ECG signal, and chaotic, time, and frequency domain features were subtracted, and estimation methods were applied. Different methods of machine learning in blood pressure estimation are compared. Dynamic learning methods such as Recurrent Neural Network (RNN), Nonlinear Autoregressive Network with Exogenous Inputs Neural Networks NARX-NN and Long-Short Term Memory Neural Network (LSTM-NN) used. Estimation results have been evaluated with performance criteria. Systolic Blood Pressure (SBP) error mean +/- standard deviation = 0.0224 +/- (2.211), Diastolic Blood Pressure (DBP) error mean +/- standard deviation = 0.0417 +/- (1.2193) values have been detected in NARX artificial neural network. The blood pressure estimation results are evaluated by the British Hypertension Society (BHS) and American National Standard for Medical Instrumentation ANSI/AAMI SP10: 2002. Finding the most accurate and easy method in blood pressure measurement will contribute to minimizing the errors. (C) 2020 Elsevier Ltd. All rights reserved.Öğe A Novel Blood Pressure Estimation Method with the Combination of Long Short Term Memory Neural Network and Principal Component Analysis Based on PPG Signals(Springer International Publishing Ag, 2020) Senturk, Umit; Polat, Kemal; Yucedag, IbrahimThe worldwide high blood pressure-related mortality rate is increasing. Alternative measurement methods are required for blood pressure measurement. There are similarities between blood pressure and photoplethysmography (PPG) signals. In this study, a novel blood pressure estimation methods based on the feature extracted from the PPG signals have been proposed. First of all, 12-time domain features have extracted from the raw PPG signal. Secondly, raw PPG signals have been applied to Principal Component Analysis (PCA) to obtain 10 new features. The resulting features have been combined to form a hybrid feature set consisting of 22 features. After features extraction, blood pressure values have automatically been predicted by using the Long Short Term Memory Neural Network (LSTM-NN) model. The prediction performance measures including MAE, MAPE, RMSE, and IA values have been used. While the combination of 12-time domain features from PPG signals and LSTM has obtained the MAPE values of 0,0547 in the prediction of blood pressures, the combination of 10-PCA coefficients and LSTM has achieved the MAPE value of 0,0559. The combination model of all features (22) and LSTM has obtained the MAPE values of 0,0488 in the prediction of blood pressures. The achieved results have shown that the proposed hybrid model based on combining PCA and LSTM is very promising in the prediction of blood pressure from PPG signals. In the future, the proposed hybrid method can be used as a wearable device in the measurement of blood pressure without any calibration.