Detection and classification of epileptic EEG signals by the methods of nonlinear dynamics

Lu, Xiaojie; Zhang, Jiqian; Huang, Shoufang; Lu, Jun Q.; Ye, Mingquan; Wang, Maosheng

doi:10.1016/j.chaos.2021.111032

Cited by 31 publications

(9 citation statements)

References 30 publications

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“…In addition to quantifying and modeling observations in laboratory animals, researchers can perform whole-region simulations of the human brain based on noninvasive imaging data ( Lynn and Bassett, 2019 ). The scalp EEG is more convenient to collect and the cost is lower than other types of data ( Lu et al, 2021 ), so a brain network is built by using scalp EEG in this paper. Transfer entropy (TE) is an information-theoretic measure method originally introduced by Schreiber (2000) to evaluate effective connectivity and it is often used to estimate “information flow” in the brain and analyze EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics

Wang

et al. 2023

Front. Neurosci.

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Lots of studies have been carried out on characteristic of epileptic Electroencephalograph (EEG). However, traditional EEG characteristic research methods lack exploration of spatial information. To study the characteristics of epileptic EEG signals from the perspective of the whole brain，this paper proposed combination methods of multi-channel characteristics from time-frequency and spatial domains. This paper was from two aspects: Firstly, signals were converted into 2D Hilbert Spectrum (HS) images which reflected the time-frequency characteristics by Hilbert-Huang Transform (HHT). These images were identified by Convolutional Neural Network (CNN) model whose sensitivity was 99.8%, accuracy was 98.7%, specificity was 97.4%, F1-score was 98.7%, and AUC-ROC was 99.9%. Secondly, the multi-channel signals were converted into brain networks which reflected the spatial characteristics by Symbolic Transfer Entropy (STE) among different channels EEG. And the results show that there are different network properties between ictal and interictal phase and the signals during the ictal enter the synchronization state more quickly, which was verified by Kuramoto model. To summarize, our results show that there was different characteristics among channels for the ictal and interictal phase, which can provide effective physical non-invasive indicators for the identification and prediction of epileptic seizures.

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Section: Introductionmentioning

confidence: 99%

Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics

Wang

et al. 2023

Front. Neurosci.

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“…The 20-second recordings originate from 2 electrodes (one focal and one nonfocal) and have been sampled at 512 Hz. In studies [127], [128], [129] they apply time domain analysis along with a VOLUME 11, 2023 self-regression model [127], TF analysis with TQWT [129], EMD [130], or Non-Linear analysis with Higuchi Fractal Dimension [131] for the detection of seizures or the separation of focal/non-focal EEG activity.…”

Section: Other Databases Of Eeg Epilepsy Recordingsmentioning

confidence: 99%

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

et al. 2023

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Epilepsy is the only neurological condition for which electroencephalography (EEG) is the primary diagnostic and important prognostic clinical tool. However, the manual inspection of EEG signals is a time-consuming procedure for neurologists. Thus, intense research has been made on creating machine learning methodologies for automated epilepsy detection. Also, many research or medical facilities have published databases of epileptic EEG signals to accommodate this research effort. The vast number of studies concerning epilepsy detection with EEG makes this systematic review necessary. It presents a detailed evaluation of the signal processing and classification methodologies employed on the different databases and provides valuable insights for future work. 190 studies were included in this systematic review according to the PRISMA guidelines, acquired from a systematic literature search in PubMed, Scopus, ScienceDirect and IEEE Xplore on 1st May 2021. Studies were examined based on the Signal Transformation technique, classification methodology and database for evaluation. Along with other findings, the increasing tendency to employ Convolutional Neural Networks that use a combination of Time-Frequency decomposition methodology images is noticed.

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“…3, June 2023: 3339-3347 3340 classification methods. On the other hand, they are prone to stagnation, trapped with local optimums, have large time requirements, and produce inconsistent results [11], [12]. In order to overcome traditional classification challenges and drawbacks, potential classification models are technically required.…”

Section: Introductionmentioning

confidence: 99%

Health Electroencephalogram epileptic classification based on Hilbert probability similarity

Al-Hamzawi

Al‐Shammary

Hammadi

2023

IJECE

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This paper has proposed a new classification method based on Hilbert probability similarity to detect epileptic seizures from electroencephalogram (EEG) signals. Hilbert similarity probability-based measure is exploited to measure the similarity between signals. The proposed system consisted of models based on Hilbert probability similarity (HPS) to predict the state for the specific EEG signal. Particle swarm optimization (PSO) has been employed for feature selection and extraction. Furthermore, the used dataset in this study is Bonn University's publicly available EEG dataset. Several metrics are calculated to assess the performance of the suggested systems such as accuracy, precision, recall, and F1-score. The experimental results show that the suggested model is an effective tool for classifying EEG signals, with an accuracy of up to 100% for two-class status.

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Detection and classification of epileptic EEG signals by the methods of nonlinear dynamics

Cited by 31 publications

References 30 publications

Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics

Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics

Machine Learning Algorithms for Epilepsy Detection Based on Published EEG Databases: A Systematic Review

Health Electroencephalogram epileptic classification based on Hilbert probability similarity

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