9 - EEG-based deep learning neural net for apnea detection

Abstract

Brain-computer interface (BCI) has gained popularity for few decades in identifying brain disorders such as stress, apnea, seizure, and dizziness. Early detection of such disorders with proper medication may increase the patients’ quality of life. However, manual analysis of the recorded electroencephalogram (EEG) signals collected from the scalp is a complex task. Therefore, an automated tool for the EEG signals’ analysis and classification is helpful where a significant research contribution is being made in the literature. This chapter proposes a parallel combination of convolutional neural network (CNN) and long-short-term memory (LSTM) structure for EEG signal analysis and apnea classification. The performance of the proposed model is compared with other works and assessed in terms of performance parameters.