# Wavelet-Based Multi-Class Seizure Type Classification System
## 1 Introduction
### Review of Related Work  
A considerable amount of research has been published on automated seizure detection and prediction. However, the automatic classification of seizure types has received little attention due to two main reasons: firstly, the difficulties inherent in the classification problem for seizure types, and secondly, a lack of clinical data [11]. Since the start of this century, considerable research outcomes have focused on the automation of epileptic seizure diagnoses [8; 9]. Generally, the procedure of automatic seizure analysis involves two phases: feature extraction and classification [12; 13]. Various methods have been proposed for feature extraction over time, including time-domain [14], frequency-domain [13; 15], and time-frequency domain [16]. Time-frequency methods became popular due to inclusion of both time and frequency features. Among time-frequency methods, wavelet transform (WT)-based feature extraction is the most promising method to extract robust features from EEG signals [17]. The strategies in wavelet-based feature extraction from EEGs use continuous wavelet transform (CWT) [18], discrete wavelet transform (DWT) [19], wavelet packet decomposition (WPD) [19; 20], tunable Q-factor wavelet transform (TQWT) [21; 22], and dual-tree wavelet transform (DTCWT) [23]. Regarding the availability of clinical data, it has been observed that in recent years, hospitals and universities have made appreciative efforts to encourage research on the automatic diagnosis of epileptic seizures by generating large volumes of openly available clinical EEG data. One of the most extensive publicly obtainable EEG datasets, the Temple University Hospital EEG Corpus (TUH EEG), is comprised of 14,000 subjects and has more than 25,000 clinical recordings [24]. The Corpus has various subsets, each focusing on different scopes of research interests. The TUH EEG Seizure Corpus (TUSZ) [25], one of the subsets, was created to motivate research on developing high-performance epileptic seizure detection algorithms using advances in machine learning algorithms [25]. This dataset contains manually annotated seizure events based on archival neurologist reports and careful examinations of the signals by students and neurologists from Temple University [25]. The seizure events in the TUSZ are labeled with eight different types of seizures: focal non-specific seizure (FNSZ), generalized non-specific seizure (GNSZ),simple partial seizure (SPSZ), complex partial seizure (CPSZ), absence seizure (ABSZ), tonic seizure (TNSZ), tonic-clonic seizure (TCSZ), and myoclonic seizure (MYSZ). The details of these labels are presented in Table 1. The corpus team continuously updates the corpus, and Table 2 presents the distribution of data for the last two versions of TUSZ. To the best of our knowledge, we found only eight published research studies which used TUSZ for the problem of seizure type classification; the summary is presented in Table 3. Regarding the seven (7) types of seizure classification, [PERSON] et al. [9] applied extreme gradient boosting (XGBoost) and KNN to classify the EEG signals into seven classes of seizures. The study reported F1-scores of 85.1% and 90.1% for XGBoost and \(K\)-nearest neighbor (KNN), respectively. Similarly, [PERSON] et al. [26] developed a deep learning model known as neural memory networks (NMN) to classify seven types of seizures. The study reported a 94.50% F1-score. In another study related to the seven-class problem, [PERSON] et al. [11] applied a deep learning framework, called SeizureNet with ensemble learning and multiple DenseNets that achieved a 95% F1-score. [PERSON] et al. [27] extracted EEG image features using a pretrained Google Inception 3 and classified them using support vector machine (SVM), achieving an accuracy of 88.3% to classify seven types of seizure classes and a normal class.