Convolutional neural network for detection and classification of seizures in clinical data

TL;DR

This paper develops a convolutional neural network trained on heterogeneous clinical EEG data to improve seizure detection and classification, achieving better sensitivity and specificity than existing tools.

Contribution

The study introduces a CNN trained on diverse clinical EEG data for seizure detection, addressing the limitations of previous models trained on clean, homogeneous data.

Findings

Sensitivity of 0.68 achieved

Specificity of 0.67 achieved

Improved detection performance over existing tools

Abstract

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives, the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on a very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, the classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data.