Assessment of Heart Rate and Respiratory Rate for Perioperative Infants Based on ELC Model

A novel optical fiber sensor using a mesh microbend optical fiber sensor to measure the perioperative heart rate (HR) and respiratory rate (RR) frequency signals was developed by our team. The feasibility of the sensor was evaluated in 10 infants in the perioperative period. We used traditional algorithms, such as Fast Fourier Transformation (FFT) and Wavelet Transformation (WT) to remove the noise and extract the features of the acquired HR and RR signals. However, the nonlinear fitting abilities of those traditional algorithms failed to completely remove the noise hence it was difficult to extract the features effectively. In this paper, we propose a deep learning model EMD-LSTM-CNN (ELC) to process both HR and RR based on Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), and Empirical Modal Decomposition (EMD) methods. The trend term is extracted by EMD from HR and RR. The CNN and LSTM are applied to extract features and process them respectively. The experimental results show that the deep learning model has a better result compared with the traditional FFT and WT algorithms. The proposed model demonstrates compliance with the current standard physiological monitoring method in measuring non-stationary vibration signals such as HR and RR, which promises potential clinical applications in the future.