Comparative study on artificial neural network with multiple regressions for continuous estimation of blood pressure.

There are many studies about cuffless and continuous blood pressure estimation using pulse transit time (PTT). In this study, we proposed the modeling method which could estimate systolic BP (SBP) conveniently and indirectly using PTT and some biometric parameters. 45 people participated in this study and we measured PTT using photoplethysmography (PPG) and electrocardiogram (ECG) signals and biometric parameters such as weight, height, body mass index (BMI), length of arm and circumference of arm. Before modeling, we selected variables as predictors using statistical analysis. With these parameters, we compared artificial neural network (ANN) with multiple regressions as an estimating method of BP. We evaluated the mean differences and standard deviations between estimated value and reference value, acquired from a KEDA-approved device. The results showed that the ANN had better accuracy than the multiple regression. ANN's estimation satisfied AAMI standard as a BP device.