An IoT-Based Intelligent Selection of Multidomain Feature for Smart Healthcare Using Reinforcement Learning in Schizophrenia

Currently, the health industry by the Internet of Things (IoT) is developing rapidly, and electroencephalogram (EEG) signal has become a bridge for human-machine communication. EEG signal feature selection is a key link in brain nerves research and has important practical application value. Therefore, we propose an IoT-based intelligent selection of multidomain feature for multimodal transformer EEG signal using reinforcement learning in Schizophrenia. First of all, the features of the multimodal EEG signal sequence by the IoT were extracted from time domain, frequency domain, and spatial domain; second, the average pooling layer was added to improve the design of the transformer model for deep feature extraction of EEG signal; finally, reinforcement learning was introduced to run operation with the extracted features as input and the improved transformer model as agent. At the same time, entropy and Pearson correlation coefficient calculation were introduced to select feature subsets, and feature intelligent selection of EEG signal in multiple domains was completed through interaction between the agent and the environment. Experiments were performed on DEAP, EEG Motor Movement/Imagery Data set, BCI2008 competition data set, and data set of a hospital. The results show that the feature extraction and feature visualization effect of the proposed algorithm is good, and the feature selection precision is as high as about 90%, and the average time is only about 13 s. These findings indicate the feasibility of the proposed feature intelligent selection algorithm in the study of EEG signal in schizophrenia, which provides a good basis for further study on the integration of IoT with the healthy industry.