Signal and Data Processing

Blood pressure is one of the vital signs. Specially, it is crucial for some cases such as hypertension patients and it should be monitored continuously in ICU/CCU. It must be noted that current systems to measure blood pressure, often require trained operators. As an example, in post-hospital cares, blood pressure control is difficult except with the presence of a nurse or use of a device that minimizes the patient's involvement in the measurements. In this way, Photoplotysmography (PPG), which is a noninvasive method for pulse wave recording, seems to be ideal to make simple tools for blood pressure measurement in home care. In other words, it is so helpful or rather necessary to design a non-invasive, cuff-less, subject-independent system for blood pressure measurement.
In this study, two optical sensors were located on the finger and the wrist. Twenty healthy volunteers in different situations were examined to record PPG signals. Also, blood pressure values were measured by cuff-based noninvasive blood pressure system on left arm as a reference value. Recorded signals were filtered and processed in MATLAB R2014a software. To promote the estimation accuracy and subject-independency, 16 temporal features in addition to the pulse transit time (PTT) were extracted from the wrist PPG signal. To estimate blood pressure values, three neural networks were used as the estimator: Feedforward Neural Network (FFN), Redial Basis Function Neural Network (RBFN) and General Regression Neural Network (GRNN). After comparison of their results; the General Regression Neural Network was used for blood pressure estimation. The MSE errors estimated by the best estimator, were 0.11±1.18 mmHg and 0.15±2.3 mmHg for systole and diastole pressure respectively.