Electroencephalographic Slowing: A Source of Error in Automatic Seizure Detection

TL;DR

This paper investigates how EEG slowing events can cause errors in automatic seizure detection and introduces a specialized database to improve differentiation between seizure-related slowing and independent slowing.

Contribution

The study presents a new EEG slowing database and demonstrates improved seizure detection accuracy by including slowing events in training data.

Findings

77% sensitivity with all three sample types

43% sensitivity with only seizure and background samples

Slowing events significantly impact seizure detection accuracy

Abstract

Although a seizure event represents a major deviation from a baseline electroencephalographic signal, there are features of seizure morphology that can be seen in non-epileptic portions of the record. A transient decrease in frequency, referred to as slowing, is a generally abnormal but not necessarily epileptic EEG variant. Seizure termination is often associated with a period of slowing between the period of peak amplitude and frequency of the seizure and the return to baseline. In annotation of seizure events in the TUH EEG Seizure Corpus, independent slowing events were identified as a major source of false alarm error. Preliminary results demonstrated the difficulty in automatic differentiation between seizure events and independent slowing events. The TUH EEG Slowing database, a subset of the TUH EEG Corpus, was created, and is introduced here, to aid in the development of a seizure detection tool that can differentiate between slowing at the end of a seizure and an independent non-seizure slowing event. The corpus contains 100 10-second samples each of background, slowing, and seizure events. Preliminary experiments show that 77% sensitivity can be achieved in seizure detection using models trained on all three sample types compared to 43% sensitivity with only seizure and background samples.