Applying CLDNN to Time-Frequency Image of EEG Signals to Predict Depth of Anesthesia

Abstract

Convolutional neural network (CNN) have been widely used in various fields in recent years. However, the CNN method is rarely used in EEG studies to assess the depth of anesthesia (DOA) in patients. In this study, EEG signal is used as the input to the convolutional, long short-term memory, fully connected deep neural networks (CLDNN) to predict DOA using continuous wavelet transform (CWT). According to the bispectral (BIS) index and signal quality index (SQI) measured by medical equipment, the anesthesia state is divided into anesthesia light (AL), anesthesia OK (AO), anesthesia deep (AD). The computing window of CWT is 120s. Moreover, 75% overlapped computing window is set to enrich medical data. Through different models, the epoch, timestep and input size of the CWT image were changed to get the best experimental results: AL was 82%, AO was 89%, and AD was 87%. The overall accuracy of the model is 87.79%, and AL and AD can be fully predicted.