The classification of flash visual evoked potential based on deep learning

• Published in: BMC medical informatics and decision making Vol. 23; no. 1; pp. 13 - 11
• Main Authors: Liang, Na, Wang, Chengliang, Li, Shiying, Xie, Xin, Lin, Jun, Zhong, Wen
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.01.2023; BioMed Central; BMC
• Subjects: Algorithms; Alzheimer's disease; Analysis; Artificial Intelligence; Big Data; Classification; Convolutional neural networks; Deep Learning; Electrodes; Electrophysiology; Evoked Potentials, Visual; Feature extraction; FVEP; Humans; Knowledge; Labeling; Labels; Machine learning; Medical screening; Neural networks; Neurologic Examination; Optic nerve; Out-of-distribution detection; Retinitis; Retinitis pigmentosa; Retrospective Studies; Technicians; Technology application; Visual discrimination learning; Visual evoked potentials; Visual perception; Waveforms; China; United States > US; Deep learning; Out-of-distribution detection; FVEP; Convolutional neural networks
• DOI: 10.1186/s12911-023-02107-5
• ISSN: 1472-6947

Visual electrophysiology is an objective visual function examination widely used in clinical work and medical identification that can objectively evaluate visual function and locate lesions according to waveform changes. However, in visual electrophysiological examinations, the flash visual evoked potential (FVEP) varies greatly among individuals, resulting in different waveforms in different normal subjects. Moreover, most of the FVEP wave labelling is performed automatically by a machine, and manually corrected by professional clinical technicians. These labels may have biases due to the individual variations in subjects, incomplete clinical examination data, different professional skills, personal habits and other factors. Through the retrospective study of big data, an artificial intelligence algorithm is used to maintain high generalization abilities in complex situations and improve the accuracy of prescreening. A novel multi-input neural network based on convolution and confidence branching (MCAC-Net) for retinitis pigmentosa RP recognition and out-of-distribution detection is proposed. The MCAC-Net with global and local feature extraction is designed for the FVEP signal that has different local and global information, and a confidence branch is added for out-of-distribution sample detection. For the proposed manual features,a new input layer is added. The model is verified by a clinically collected FVEP dataset, and an accuracy of 90.7% is achieved in the classification task and 93.3% in the out-of-distribution detection task. We built a deep learning-based FVEP classification algorithm that promises to be an excellent tool for screening RP diseases by using FVEP signals.