# Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmark

## TL;DR

This paper demonstrates the effectiveness of machine learning algorithms in classifying seizure types from EEG signals, establishing a benchmark with high accuracy on a large dataset for scalp EEG-based seizure classification.

## Contribution

It introduces a comprehensive evaluation of various machine learning techniques and preprocessing methods for multi-class seizure classification using the TUH EEG corpus, setting a new benchmark.

## Key findings

- Achieved a weighted F1 score of 0.901 for seizure-wise validation.
- Achieved a weighted F1 score of 0.561 for patient-wise validation.
- Provided a thorough search space exploration for optimal model configurations.

## Abstract

Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of drugs but also the range of activities a patient can safely engage in. With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification. We used the recently released TUH EEG seizure corpus (V1.4.0 and V1.5.2) and conducted a thorough search space exploration to evaluate the performance of a combination of various pre-processing techniques, machine learning algorithms, and corresponding hyperparameters on this task. We show that our algorithms can reach a weighted $F1$ score of up to 0.901 for seizure-wise cross validation and 0.561 for patient-wise cross validation thereby setting a benchmark for scalp EEG based multi-class seizure type classification.