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Transfer learning to detect neonatal seizure from electroencephalography signals

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Abstract

This paper offers a solution to the problem of detecting neonatal seizures via a transfer learning technique that judiciously reconstructs pre-trained deep convolution neural networks (p-DCNN), including alexnet, resnet18, googlenet, densenet, and resnet50. Multichannel electroencephalography (EEG) signals are converted to colour images for feeding them as an input for the p-DCNN. A deep neural network (DNN) such as a convolution neural network (CNN) may be directly used instead of transfer learning-based networks. However, a DNN requires too much training data, too much training time, and a computer with high-performance computational capability. The DNN also has several user-supplied hyper-parameters that must be tuned to obtain desirable classification success. To prevent these drawbacks, we propose a transfer learning technique to solve the neonatal seizures detection problem. Results of simulations and the statistical analysis enable us to devise a transfer learning technique employed for seizure detection.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Iskenderun Technical University, Hatay, Turkey

    Abdullah Caliskan

  2. Department of Electrical and Electronics Engineering, Institute of Science and Engineering, Iskenderun Technical University, Hatay, Turkey

    Suleyman Rencuzogullari

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  1. Abdullah Caliskan
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  2. Suleyman Rencuzogullari
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Correspondence to Abdullah Caliskan.

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Caliskan, A., Rencuzogullari, S. Transfer learning to detect neonatal seizure from electroencephalography signals. Neural Comput & Applic 33, 12087–12101 (2021). https://doi.org/10.1007/s00521-021-05878-y

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