Seizure Type Classification Using EEG Signals and Machine Learning: Setting a Benchmark

Roy, Subhrajit; Asif, Umar; Tang, Jianbin; Harrer, Stefan

doi:10.1109/spmb50085.2020.9353642

Cited by 56 publications

(57 citation statements)

References 18 publications

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“…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].…”

Section: Review Of Related Workmentioning

confidence: 99%

“…18-Jun-2021 In spite of good performance reported in 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%.…”

Section: Review Of Related Workmentioning

confidence: 99%

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Wavelet Based Multi-Class Seizure Type Classification System

Albaqami

Hassan²,

Datta

2022

SSRN Journal

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Section: Review Of Related Workmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

Wavelet Based Multi-Class Seizure Type Classification System

Albaqami

Hassan²,

Datta

2022

SSRN Journal

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“…The multi-class seizure type classification was implemented using the version 1.5.2 of Temple University Hospital (TUH) EEG Seizure Corpus (Roy et al, 2019). The train set of TUH EEG Seizure Corpus v1.5.2 collected 592 patient EEGs and recorded a total of 2,377 seizures.…”

Section: Eeg Databasementioning

confidence: 99%

“…Despite many research cased combing feature engineering and machine learning have achieved high recognition accuracy on public databases. The modeling capabilities of machine learning models are limited, and severe degradation has occurred on large databases (Roy et al, 2019). The exciting thing is that, owing to the accumulation of database, deep learning models that can process big data through self-learning have been proven to be similar to humans in biomedical data analysis (Rajpurkar et al, 2017;Yıldırım et al, 2018;Arsenovic et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG

et al. 2021

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Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications. In order to improve the clinical adaptability of the algorithm, this paper proposes an adversarial learning-driven domain-invariant deep feature representation method, which enables the hybrid deep networks (HDN) to reliably identify seizure types. In the train phase, we first use the labeled multi-lead EEG short samples to train squeeze-and-excitation networks (SENet) to extract short-term features, and then use the compressed samples to train the long short-term memory networks (LSTM) to extract long-time features and construct a classifier. In the inference phase, we first adjust the feature mapping of LSTM through the adversarial learning between LSTM and clustering subnet so that the EEG of the target patient and the EEG in the database obey the same distribution in the deep feature space. Finally, we use the adjusted classifier to identify the type of seizure. Experiments were carried out based on the TUH EEG Seizure Corpus and CHB-MIT seizure database. The experimental results show that the proposed domain adaptive deep feature representation improves the classification accuracy of the hybrid deep model in the target set by 5%. It is of great significance for the clinical application of EEG automatic analysis equipment.

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“…Patients with epilepsy can display several seizure types [3], and the monitoring of seizures for forecasting and detection is a subject of intense research [4], [5]. Recent advances in the field use elaborate methods from machine learning to analyse EEG timeseries and automatically extract the most relevant features from the signals to perform the detection or classification task [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Features importance in seizure classification using scalp EEG reduced to single timeseries

Naze

Tang

Kozloski³

et al. 2021

Preprint

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Seizure detection and seizure-type classification are best performed using intra-cranial or full-scalp electroencephalogram (EEG). In embedded wearable systems however, recordings from only a few electrodes are available, reducing the spatial resolution of the signals to a handful of timeseries at most. Taking this constraint into account, we tested the performance of multiple classifiers using a subset of the EEG recordings by selecting a single trace from the montage or performing a dimensionality reduction over each hemispherical space. Our results support that Random Forest (RF) classifiers lead most efficient and stable classification performances over Support Vector Machines (SVM). Interestingly, tracking the feature importances using permutation tests reveals that classical EEG spectrum power bands display different rankings across the classifiers: low frequencies (delta, theta) are most important for SVMs while higher frequencies (alpha, gamma) are more relevant for RF and Decision Trees. We reach up to 94.3% +/- 5.3% accuracy in classifying absence from tonic-clonic seizures using state-of-art sampling methods for unbalanced datasets and leave-patients-out 3-fold cross-validation policy.

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Seizure Type Classification Using EEG Signals and Machine Learning: Setting a Benchmark

Cited by 56 publications

References 18 publications

Wavelet Based Multi-Class Seizure Type Classification System

Wavelet Based Multi-Class Seizure Type Classification System

Automatic Seizure Classification Based on Domain-Invariant Deep Representation of EEG

Features importance in seizure classification using scalp EEG reduced to single timeseries

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