Epileptic seizure detection using novel Multilayer LSTM Discriminant Network and dynamic mode Koopman decomposition

Saichand, N. Venkata; S, Gopiya Naik

doi:10.1016/j.bspc.2021.102723

Cited by 11 publications

(2 citation statements)

References 32 publications

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“…For learning the multi-scale features from the EEG, Zhang et al ( 2020 ) designed a multi-scale non-local (MNL) network with two special layers to achieve promising classification results of the seizure. Additionally, some other works (Aliyu and Lim, 2021 ; Hussain et al, 2021 ; Saichand, 2021 ) adopted the long short-term memory (LSTM) to overcome the vanishing gradient problem of the recurrent neural network and boost the feature extraction ability of the EEG signal data.…”

Section: Introductionmentioning

confidence: 99%

Positional multi-length and mutual-attention network for epileptic seizure classification

Zhang,

Liu

et al. 2024

Front. Comput. Neurosci.

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The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Positional multi-length and mutual-attention network for epileptic seizure classification

Zhang,

Liu

et al. 2024

Front. Comput. Neurosci.

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“…Although many studies have proposed deep learning (DL) based models for automated seizure detection (Saab et al 2020;Shoeibi et al 2021;Abdelhameed and Bayoumi 2021;Thuwajit et al 2021;Khalkhali et al 2021;Rashed-Al-Mahfuz et al 2021;Mahajan, Somaraj, and Sameer 2021;Saichand et al 2021;Shen et al 2022;Gao et al 2022), several challenges still remain unsolved. First, these studies train their proposed model in a supervised approachi.e.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis

Armanfard

2023

AAAI

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Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. The proposed method considers local structural and contextual information embedded in EEG graphs by employing positive and negative sub-graphs. We train our method through minimizing contrastive and generative losses. The employ of local EEG sub-graphs makes the algorithm an appropriate choice when accessing to the all EEG channels is impossible due to complications such as skull fractures. We conduct an extensive set of experiments on the largest seizure dataset and demonstrate that our proposed framework outperforms the state-of-the-art methods in the EEG-based seizure study. The proposed method is the only study that requires no access to the seizure data in its training phase, yet establishes a new state-of-the-art to the field, and outperforms all related supervised methods.

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Enhancing EEG signals classification using LSTM‐CNN architecture

Omar,

Kimwele,

Olowolayemo

et al. 2023

Engineering Reports

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Epilepsy is a disorder that interferes with regular brain activity and can occasionally cause seizures, odd sensations, and momentary unconsciousness. Epilepsy is frequently diagnosed using electroencephalograph (EEG) records, although conventional analysis is subjective and prone to error. The dynamic and non‐stationary nature of EEG structure restricted the performance of Deep Learning (DL) approaches used in earlier work to improve EEG classification. Our multi‐channel EEG classification model, dubbed LConvNet in this paper, combines Convolutional Neural Networks (CNN) for extracting spatial features and Long Short‐Term Memory (LSTM) for identifying temporal dependencies. To discriminate between epileptic and healthy EEG signals, the model is trained using open‐source secondary EEG data from Temple University Hospital (TUH). Our model outperformed other EEG classification models employed in comparable tasks, such as EEGNet, DeepConvNet, and ShallowConvNet, which had accuracy rates of 86%, 96%, and 78%, respectively. Our model attained an amazing accuracy rate of 97%. During additional testing, our model also displayed excellent performance in trainability, scalability, and parameter efficiency.

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Epileptic seizure detection using novel Multilayer LSTM Discriminant Network and dynamic mode Koopman decomposition

Cited by 11 publications

References 32 publications

Positional multi-length and mutual-attention network for epileptic seizure classification

Positional multi-length and mutual-attention network for epileptic seizure classification

Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis

Enhancing EEG signals classification using LSTM‐CNN architecture

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