In the previous paper, we built a convolutional neural network to differentiate normal VEPs from abnormal VEPs from signals obtained from multifocal VEP examination.7 Still images are more suitable for the convolutional neural network. In data with dynamic properties, a combination of the convolutional and recurrent neural network was more suitable. The recurrent neural network has been proven to be useful in analyzing data, such as clinical notes,23,24 anesthesia parameters,25 and cardiographs.26 Here, we combined a convolutional neural network and recurrent neural network with the assumption that the former can differentiate static images and the latter can recognize dynamic patterns. We chose the long-short memory layer because of its property of selectively remembering and forgetting patterns for long and short durations of time.