Automatic BASED scoring on scalp EEG in children with infantile spasms using convolutional neural network - Fan, Yuying; Chen, Duo; Wang , Hua; Pan , Yijie; Peng , Xueping; Liu , Xueyan; Liu, Yunhui (Frontiers in Molecular Biosciences, 2022)
In the past few years, the Burden of Amplitudes and Epileptiform Discharges (BASED) score has been used as a reliable, accurate and feasible Electroencephalogram (EEG) grading scale for infantile spasms. However, the EEG manual annotation is very time-consuming, while BASED scoring is facing the same problem. Convolutional neural network (CNN) has proven its huge potential on plenty of EEG classification problems. However, there is very limited work on using CNN for BASED scoring, a challenging but vital task in diagnosis and treatment of infantile spasms. This paper proposes an automatic BASED scoring framework using EEG and deep convolutional neural network (CNN). The feasibility of CNN has been investigated for annotating the BASED score in long-term EEG recorded from 58 infantile spasms patients, and the EEG has been annotated into 4 levels (Score 5, 4, 3 and ≤ 2) according to the BASED score. The extensive experiments demonstrate that our proposed approach offers high accuracy and hence is significant in building an automatic BASED scoring algorithm. The accuracy is 96.9% in the validation set by applying multi-layer CNN to classify the EEG data as a 4-labels problem. To our best knowledge, this is the first attempt to use CNN to construct an BASED scoring-based model.