Transfer learning with DWT based clustering for blood pressure estimation of multiple patients

Abstract

In this manuscript, we focus on estimating blood pressure (BP) of multiple patients based on easily-collected pulse wave (PW) data efficiently. In detailed, we first design a Convolutional Neural Networks-Gated Recurrent Unit (CNN-GRU) model for BP estimation. Then we propose a transfer learning scheme with Discrete Wavelet Transform (DWT) based k-means clustering algorithm, the so-called TLDK method, to enhance CNN-GRU model for multiple patients. In this method, we employ k-means to cluster the patients based on approximate components of PW data obtained from DWT. Next, a base CNN-GRU model trained on one patient is transferred to other patients within each cluster by freezing parameters of partial layers. As a result, TLDK avoids training models individually for each patient from scratch and improves the generalization ability of models. The numerical results based on MIMIC dataset show that the proposed method is time-saving while achieving superior performance. Therefore, the proposed method can be applied efficiently for convenient BP estimation for multiple patients.