Classification of 1D and 2D EEG Signals for Seizure Detection in the Newborn Using Convolutional Neural Networks - Açıkoğlu, Merve; Tuncer, Seda Arslan (My University, 2022)
Unlike adults, neonates do not always show clinical symptoms during seizures.\rTherefore, uncontrolled seizures lead to severe brain damage. Timely recognition of\rseizures plays a crucial role for neonates. In this study, a deep transfer learning\rapproach was proposed for automatic detection of seizures on the C4-P4 channel\rusing electroencephalography (EEG) signals from neonates. The EEG signals were\rused in 1D and 2D dimensions to ensure performance, robust functionality, and a\rclinically acceptable level of detection accuracy. The pre-trained deep learning\rmodels Alexnet, ResNet, GoogleNet and VggNet were used in the study.\rSpectrograms were obtained by converting 1-dimensional signal data into 2-\rdimensional images, and then classification was performed for both the 1D and 2D\rdatasets. For 1D classification, the highest performance was obtained by VggNet\rarchitecture with 91.67%, while 2D classification was obtained by AlexNet and\rResNet architecture with 95.83%. The use of spectrograms significantly improved\rclassification performance and made neonatal seizure detection and decision-making\rmore clinically reliable.
Keywords: Convolutional Neural Network; Newborn Seizure; EEG Signal; C4-P4 channel