A New Approach for Blood Pressure Monitoring based on ECG and PPG Signals by using Arti?cial Neural Networks

Background: Pulse transit time has been demonstrated as one of the potential parameters for a cuffless blood pressure measurement. The accuracy of this method depends on the initial calibration that is obtained by several measurements. The aim of this study was to employ artificial neural network in order to estimate the blood pressure based on PTT. PTT is defined as the time delay between the R-wave of the ECG and the peak of the pulse wave in the finger. To train the ANN for modeling the blood pressure, this study used a database containing 65 subjects. For each subject, BP was taken several times in different condition. The trained ANN was capable of establishing a function between the PTT and the BP as an input and a response, respectively. The results of estimating BP were compared with the results of sphygmomanometer method and the error rate was calculated. The absolute error and error percentage in systolic blood pressure between cuff method and the present method were 5.412.63 mmHg, 4.091.59% and for diastolic blood pressure were 7.012.52 mmHg, 6.882.43%. The results indicated that the BP measurement by cuff method and BP predicted with trained ANN differ by only less than 10%. It is obvious that the neural network prediction fit properly to the cuff results. The results of proposed method were closely in agreement with the results of the sphygmomanometer cuff. So the present method could be applied as an effective tool for the blood pressure estimation.