1.1. Background
Epilepsy is the most widespread brain disease among children and adults after stroke [
1]. It is defined as “a sudden and recurrent brain malfunction and is a disease that reflects an excessive and hypersynchronous activity of the neurons within the brain” [
2]. Over 60 million of the world’s population are diagnosed with epilepsy, whose defining feature is recurrent seizures. Such seizure attacks impair the brain’s normal functions, leading the patient to be vulnerable and unsafe.
Seizures are medically classified into two main categories—focal seizures or generalized seizures—depending on the extent to which regions of the brain are affected. Focal seizures are seizures that originate and affect a circumscribed region of the brain. Focal seizures are further classified into simple or complex, based on the patient’s level of awareness. Generalized seizures, on the other hand, involve most areas of the brain. Based on motor and non-motor symptoms, generalized seizure classifications can be absence, tonic, atonic, clonic, tonic-clonic, or myoclonic seizures [
3,
4]. Classification of seizure is very essential for accurate diagnosis and treatment.
Identifying the type of seizure, although sometimes difficult, can be done by clinical observation and referencing medical history and demographic information, and is supported by general brain imaging techniques such as EEG, magnetoencephalography (MEG), and fMRI [
5,
6]. EEG is the most practical and cost-effective tool to diagnose epilepsy currently [
7]. Video-EEG monitoring is often required to support the decision for seizure classification [
8].
For treatment, seizures can be controlled in most cases (up to 70%) of patients by consuming medication to achieve a steady-state concentration in the blood. Surgical intervention is another option for certain conditions. For up to 20% of epileptic patients, there is no medical treatment that exists to control seizures [
2]. The accurate identification of the type of seizures influences medication choice and provides information to patients, families, researchers, and clinicians caring for patients with epilepsy [
4,
9].
It is a challenging task to classify the type of seizure accurately. Several factors make the classification difficult. Firstly, some types of seizures share the same clinical and EEG symptoms. For instance, it has been shown that even for a highly experienced neurologist, sometimes it is hard to distinguish between focal and generalized seizures [
10]. Secondly, in some cases, it is required to perform long-term monitoring (i.e., video-EEG monitoring), which may last for days [
7]. Therefore, manual analysis of these long recordings requires a substantial amount of effort and time from neurologists.
In addition, signal interpretation is known to have a low inter-rater agreement, which fully depends on the level of expertise of the expert. Moreover, inter-subject variability significantly adds to the difficulties associated with the diagnosis of an epileptic seizure, leading to a variety of manifestations of the same type of seizures across different patients, and sometimes for the same individual over time. Finally, signal artifacts also hinder the correct interpretation of EEG. With these challenges, in a field that already has a shortage of healthcare experts, computer-aided diagnostic (CAD) methods have great potential to support decision making in the diagnosis of such a critical disease.
1.2. 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, Roy 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, Aristizabal 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, Asif et al. [
11] applied a deep learning framework, called SeizureNet with ensemble learning and multiple DenseNets that achieved a 95% F1-score.
Raghu 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. Similarly, in [
28], a convolutional neural network (CNN) model,
AlexNet, is applied to classify EEG images based on the technique of short-time Fourier transform (STFT) to classify seven types of seizure and non-seizure class. The study yielded an accuracy of 84.06%. Liu et al. [
8] applied a hybrid bi-linear model consisting of CNN and long short-term memory (LSTM) to classify eight types of seizures. The study reported a 97.4% F1-score.
For the four-class classification of seizures, Wijayanto et al. [
29] applied empirical mode decomposition (EMD) to EEGs for feature extraction and quadratic SVM for classification. The study reported an accuracy of 95%. In another study, Ramadhani et al. [
30] applied EMD, Mel frequency cepstral coefficients (MFCC), and independent component analysis (ICA) to EEG data for feature extraction and SVM for classification of four classes of seizures and achieved 91.4% accuracy. For three classes of seizure classification, Saric et al. [
31] developed a field programmable gate array (FPGA)-based framework for the classification of generalized and focal epileptic seizures using a feed-forward multi-layer neural network and achieved an accuracy of 95.14%.
In spite of the good performance reported in the aforementioned studies, it is expected that the reported techniques cannot be used in real world situations as the studies either did not report the performance when tested on data from new patients or reported lower performance. Out of the eight studies presented in
Table 3, only two studies [
9,
11] considered the generalization of their proposed techniques. Both studies mentioned a considerable decrease in the performance of their system, where the performance decreased by 45%. This shows that there is still a large gap for advancement for better generalization capability for the classification systems.
It is interesting to observe from
Table 3 that the authors of these studies chose a different number of seizure classes, ranging from a three-class problem to an eight-class problem for seizure type classification. The reason behind the choice of the number of classes is not discussed in most of these studies. The authors of [
9,
11,
26,
27,
28] excluded the seizure type MYSZ from their experiments because the signals of this type were only recorded from two patients (see
Table 2). However, in [
8], the authors chose to utilize all seizure types in the dataset regardless of the number of patients.
Table 3 presents the investigated seizure types for each study.
It can be observed from
Table 1 that in TUSZ, there are six specific types of seizures and two non-specific general types. From a pathological point of view, these types are not completely disjoint but form a hierarchical sub-grouping [
4,
26]. It has been stated in [
7] that when there is inadequate evidence to label the type of seizure confidently, the corpus team tends to label an event as either focal non-specific or generalized non-specific based on the seizure’s focality and locality [
25]. Both of these types are not medically distinct from one another, whereas SPNS and CPSZ are more specific types of FNSZ, and ABSZ, TNSZ, TNSZ, and MYSZ are more specific types of GNSZ [
4,
26]. Thus, considering the label FNSZ as a unique type of seizure against the specific focal types CPSZ and SPSZ might cause the classifier not to perform well, and similarly for the classification of GNSZ.
Therefore, in this study, we are considering two different classification problems. In the first problem, each label is considered in the dataset as a unique seizure type, and results are compared with the existing state-of-the-art results. On the other hand, the second problem is the introduction of a new challenge, which is more important pathologically, that deals with the specific seizure type classification to investigate the effect of the non-specific labels in TUSZ (five-class classification).
In order to solve the above mentioned problems, we propose a novel technique that focuses on wavelet-based machine learning methods for automatic seizure type classification in multi-channel EEG recordings. We only utilized EEG data and decomposed the EEG signals into different levels of components using DTCWT to extract specific features from these decomposed components. We used shift-invariant DTCWT for feature extraction from a biomedical signal and its classification, which is done for the first time in the literature for seizure type classification. Moreover, we tested our proposed technique on the largest available seizure EEG database, containing various types of epileptic seizures. In order to ensure the effectiveness and generalization of our technique, we thoroughly tested our proposed technique across subjects in addition to normal testing. The experimental results show that our proposed novel technique performs well for both problems of seizure-type classification.
The rest of this paper is organized as follows:
Table 2 discusses information about the data utilized in this research and the details of our proposed technique.
Table 3 presents the evaluation methodology and the analyses of the obtained results. A thorough discussion is provided in
Table 4.
Table 5 concludes the article with a future research plan.
Table 3.
Summary of existing state-of-the-art techniques for seizure classification.
Table 3.
Summary of existing state-of-the-art techniques for seizure classification.
Method | No. of Seizure Classes | Classes Considered | Eigenschaften | Performance (%) |
---|
Transfer learning Inceptionv3 [27] | 8 * | GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, TNSZ, TCSZ, NORM | SFFT | 88.3 Accuracy |
AlexNet [28] | 8 * | GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, TNSZ, TCSZ, NORM | SFFT | 84.06 Accuracy |
CNN+LSTM+MLP [8] | 8 | GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, TNSZ, TCSZ, MYSZ | SFFT | 97.40 F1-score |
SeizureNet Ensemble CNNs [11] | 7 | GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, TNSZ, TCSZ | FFT | 95 F1-score |
Plastic NMN [26] | 7 | GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, TNSZ, TCSZ | FFT | 94.5 F1-score |
K-NN [9] | 7 | GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, TNSZ, TCSZ | FFT | 90.1 F1 |
XGBoost [9] | 7 | GNSZ, FNSZ, SPSZ, CPSZ, ABSZ, TNSZ, TCSZ | FFT | 85.1 F1-score |
SVM [30] | 4 * | GNSZ, FNSZ, TCSZ, NORM | MFCC+HD+ICA | 91.4 Accuracy |
FPGA-based ANN [31] | 3 * | GNSZ, FNSZ, NORM | CWT | 95.14 Accuracy |
SVM [29] | 4 | GNSZ, FNSZ, SPSZ, TNSZ | EMD | 95 Accuracy |
Table 4.
EEG channel names included in our study.
Table 4.
EEG channel names included in our study.
# | Channels | # | Channels |
---|
1 | FP1-F7 | 2 | F7-T3 |
3 | T3-T5 | 4 | T5-O1 |
5 | FP2-F8 | 6 | F8-T4 |
7 | T4-T6 | 8 | T6-O2 |
9 | T3-C3 | 10 | C3-CZ |
11 | CZ-C4 | 12 | C4-T4 |
13 | FP1-F3 | 14 | F3-C3 |
15 | C3-P3 | 16 | P3-O1 |
17 | FP2-F4 | 18 | F4-C4 |
19 | C4-P4 | 20 | P4-O2 |
Table 5.
Hyperparameters for LightGBM classifier.
Table 5.
Hyperparameters for LightGBM classifier.
Hyperparameter | Value |
---|
boosting type | gbdt |
Learning_rate | 0.2 |
n_estimators | 1500 |
colsample_bytree | 0.13151 |
importance_type | split |
num_leaves | 31 |
subsample | 0.8 |