CNN based framework for detection of epileptic seizures

Sameer, Mustafa; Gupta, Bharat

doi:10.1007/s11042-022-12702-9

Cited by 21 publications

(5 citation statements)

References 41 publications

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“…CNN was also used in [19] for seizure classification using plotted EEG images formed from EEG signals. 1D CNN, a variant of CNN, was used in [4] for ES detection by using the raw EEG of the Bonn dataset. In another study [5], a transfer learning concept was used in the ES detection process, where a pre-trained AlexNet CNN model was used on raw EEG data.…”

Section: Related Workmentioning

confidence: 99%

“…Epilepsy has been a subject of extensive research in the computational intelligence domain over the last few decades ( [2][3][4][5][6]) for automated ES detection and diagnosis. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools for analyzing EEG for the diagnosis of neurological disorders such as autism and emotion [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Existing EEG-based ES studies have explored various techniques to process the EEG signals, extract relevant features, and use different ML/DL methods for ES detection. Some studies have focused on using raw EEG signals (e.g., [4][5][6][11][12][13]) and employed DL learning methods for feature extraction with embedded methods of individual DL methods. Furthermore, recent studies have explored various feature extraction methods to capture discriminative information from raw EEG signals [3,11,[14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network

Das,

Mumu,

Akhand

et al. 2024

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Electroencephalogram (EEG) has emerged as the most favorable source for recognizing brain disorders like epileptic seizure (ES) using deep learning (DL) methods. This study investigated the well-performed EEG-based ES detection method by decomposing EEG signals. Specifically, empirical mode decomposition (EMD) decomposes EEG signals into six intrinsic mode functions (IMFs). Three distinct features, namely, fluctuation index, variance, and ellipse area of the second order difference plot (SODP), were extracted from each of the IMFs. The feature values from all EEG channels were arranged in two composite feature forms: a 1D (i.e., unidimensional) form and a 2D image-like form. For ES recognition, the convolutional neural network (CNN), the most prominent DL model for 2D input, was considered for the 2D feature form, and a 1D version of CNN was employed for the 1D feature form. The experiment was conducted on a benchmark CHB-MIT dataset as well as a dataset prepared from the EEG signals of ES patients from Prince Hospital Khulna (PHK), Bangladesh. The 2D feature-based CNN model outperformed the other 1D feature-based models, showing an accuracy of 99.78% for CHB-MIT and 95.26% for PHK. Furthermore, the cross-dataset evaluations also showed favorable outcomes. Therefore, the proposed method with 2D composite feature form can be a promising ES detection method.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network

Das,

Mumu,

Akhand

et al. 2024

Information

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“…Salafian et al (2021) proposed a 1D-CNN with factor graphs as input which yielded an AUC-ROC up to 89.53 ± 0.04%. Sameer and Gupta (2022a) focused on the problem of long training time and computational power required in the development of DL architectures. The authors extracted features from an 11-layered CNN network which contained four layers of 1D convolution layer, followed by three batch normalization, one max pooling, one global average polling, drop-out, and a fully connected layer.…”

Section: Related Workmentioning

confidence: 99%

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

Handa,

Gupta,

Gupta

et al. 2023

Expert Systems

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Precise monitoring and diagnosis of epilepsy by manual analysis of EEG signals are challenging due to the low doctor‐to‐patient ratio, and shortage of medical resources. To automate this diagnosis in real‐time, EEG based Brain–Computer Interface (BCI) system with integration of artificial intelligence techniques will prove to be propitious. This work proposes an end‐to‐end, one‐dimensional atrous conv‐net‐based architecture for automatic epilepsy diagnosis using EEG signals with a conceptual framework of the EEG‐BCI system for routine monitoring and clinical use. The proposed architecture has a robust backbone of six blocks of atrous convolutional layers activated with exponential linear unit functions. The six blocks are followed by the addition of a long short‐term memory layer for automatic feature extraction and sequential EEG data analysis. The efficacy of the proposed architecture has been verified on three publicly available EEG datasets using various evaluation metrics, feature maps, test set evaluation, and ablation studies. An average training and validation accuracy of 96.16% and 90.80% has been achieved upon multiple runs for the three datasets. Ablation experiments indicate that the addition of each block contributed to increasing 17%–25% accuracy scores during the classification of epileptic and non‐epileptic EEG signals. The real‐time EEG‐BCI has been analyzed using weight optimization of the proposed architecture through the NVIDIA Tensor RT framework on a 40 GB DGX A100 NVIDIA workstation. The proposed architecture has generalized well in comparison with the existing techniques for the three EEG datasets and achieved a low training and validation loss with optimum evaluation metrics. This makes the proposed architecture suitable for future EEG‐BCI system deployment in the automatic diagnosis of epilepsy.

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“…Manocha et al [18] proposed a sequential 1-dimension convolution neural network (CNN) and earlier models such as the 1-D inception module and 1-D ResNet model to classify the EEG data as epilepsy or not. The authors in [19] presented a new 1D CNN that performs an automated feature extraction process followed by an ML classification model. In addition, 1D CNN models are essentially appropriate to process EEG time-series data.…”

Section: Literature Reviewmentioning

confidence: 99%

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

et al. 2022

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CNN based framework for detection of epileptic seizures

Cited by 21 publications

References 41 publications

Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network

Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network

One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

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