A personalized blood pressure prediction model using recurrent kernel extreme reservoir machine

Abstract

Hypertension is becoming a global epidemic for the developing world and continuous blood pressure monitoring and early diagnosis is vital for the prevention of this disease. However, in real life, patients are usually unable to maintain frequent monitoring because of reasons that include forgetfulness, human error and/or machine error. This paper presents a personalized prediction model for blood pressure using Recurrent Kernel Extreme Reservoir Machine (RKERM). This technique combines reservoir computing with RKELM to perform multistep ahead prediction. We use RKERM for blood pressure prediction and its performance is evaluated with other ELM based prediction algorithms. To evaluate our model, we use real world blood pressure data collected from Malaysian population consisting of hypertensive and non-hypertensive patients. The experimental results show that the proposed prediction mechanism has higher prediction accuracy than existing ELM methods.