# Wavelet-Based Multi-Class Seizure Type Classification System

arXiv: https://arxiv.org/abs/2203.00511 · HTML: https://mlsanity.com/paper/2203.00511/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Neural Memory Networks for Seizure Type Classification](https://mlsanity.com/paper/1912.04968/) (2019)
- [Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree](https://mlsanity.com/paper/2012.10034/) (2020)
- [An Analysis of Two Common Reference Points for EEGs](https://mlsanity.com/paper/1801.02474/) (2018)
- [Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models](https://mlsanity.com/paper/2001.06282/) (2020)
- [Optimizing Channel Selection for Seizure Detection](https://mlsanity.com/paper/1801.02472/) (2018)

## Built on by (most cited first, 2 of 2)

- [MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG](https://mlsanity.com/paper/2211.04628/) (2022)
- [DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations](https://mlsanity.com/paper/2607.11578/) (2026)

## Abstract (arXiv)

Epilepsy is one of the most common brain diseases that affect more than 1% of the world's population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and highly-specialized. Moreover, EEG manual evaluation is a process known to have a low inter-rater agreement among experts. This paper presents a novel automatic technique that involves extraction of specific features from EEG signals using Dual-tree Complex Wavelet Transform (DTCWT) and classifying them. We evaluated the proposed technique on TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with existing state-of-the-art techniques using overall F1-score due to class imbalance seizure types. Our proposed technique achieved the best results of weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification respectively, thereby setting new benchmark results for this dataset.