Detection and Severity Identification of Neonatal Seizure Using Deep Convolutional Neural Networks from Multichannel EEG Signal

Abstract

Introduction

One of the most frequent neurological conditions in newborns is neonatal seizures, which may indicate severe neurological dysfunction. These seizures may have very subtle or very modest clinical indications because patterns like oscillatory (spike) trains begin with relatively low amplitude and gradually increase over time. This becomes very challenging and erroneous if clinical observation is the primary basis for identifying newborn seizures. In this study, a diagnosis system using deep convolutional neural networks is proposed to determine and classify the severity level of neonatal seizures using multichannel neonatal EEG data.

Methods

Datasets from publicly accessible online sources were used to compile clinical multichannel EEG datasets. Various preprocessing steps were taken, including the conversion of 2D time series data to equivalent waveform pictures. The proposed models have undergone training, and evaluations of their performance were conducted.

Results

The proposed CNN was used to perform binary classification with an accuracy of 92.6%, F1-score of 92.7%, specificity of 92.8%, and precision of 92.6%. To detect newborn seizures, this model is utilized. Using the proposed CNN model, multiclassification was performed with accuracy rates of 88.6%, specificity rates of 92.18%, F1-score rates of 85.61%, and precision rates of 88.9%. The results demonstrated that the suggested strategy can assist medical professionals in making accurate diagnoses close to healthcare institutions.

Conclusion

The developed system was capable of detecting neonatal seizures and has the potential to be used as a decision-making tool in resource-limited areas with a scarcity of expert neurologists.

Keywords:

• AlexNet
• CNN
• multichannel EEG
• neonatal seizure
• severity identification

Data Sharing Statement

Link to the datasets (for Data_1, Data_2, and Data_3): https://doi.org/10.5281/zenodo.2547147.

Ethical Statement

The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This research has been approved by Wachamo University, College of Health Science (IRB: 35/2018) and the study was conducted following the Declaration of Helsinki (as revised in 2013).

Acknowledgments

The authors of this study gratefully acknowledge Helsinki University Hospital, Finland, for sharing the largest open database of EEG signals. Moreover, the authors extend gratitude to Jimma University School of Biomedical Engineering and Wachamo University Hospital’s pediatrics department staff.

Disclosure

The authors declare that they have no competing interests.