Özet:

Newborns do not always show clinical symptoms during seizures unlike adults. Therefore, uncontrolled seizures lead to serious brain damage. Timely detection of seizures plays a vital role for newborn babies. In this study, a deep transfer learning approach was proposed for automatic seizure detection on the C4-P4 channel using electroencephalography (EEG) signals of newborns. EEG signals have been used in 1D and 2D dimensions to ensure performance, robust functionality, and a clinically acceptable level of detection accuracy. Pre-trained deep learning models Alexnet, ResNet, GoogleNet and VggNet were used in the study. Spectrograms were obtained by converting 1-dimensional signal data to 2-dimensional images, and then the classification was made on both 1D and 2D data set. In 1D classification, the highest performance was obtained from VggNet architecture with 91.67%, while 2D classification was obtained from AlexNet and ResNet architecture with 95.83%. The use of spectrograms has greatly improved the classification performance and made seizure detection and decision clinically more reliable in newborns.

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