Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

Varli, Muhammet; Yılmaz, Hakan

doi:10.1016/j.jocs.2023.101943

Cited by 36 publications

(13 citation statements)

References 37 publications

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“…A hybrid model was presented by Varli et al [28] that makes use of the time sequence of EEG data in addition to time-frequency-EEG data transformations of timedependent EEG signals. To transform signals into EEG data, CWT and STFT techniques were used.…”

Section: Literature Surveymentioning

confidence: 99%

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Prasanna,

Rahman,

Bayleyegn

2023

IEEE Access

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Brain Epilepsy seizure is a critical disorder, which is an uncontrolled burst of electrical activity of brain. The early detection of brain seizure can save the life of humans. The electroencephalogram (EEG) signals may be used to automatically identify brain seizures, which is one of the most prominent solutions for this issue. However, the conventional methods are failed to classify the brain seizure effectively. So, this work implemented the Brain Epilepsy Seizure-Detection-Network (BESD-Net) using deep learning, recurrent learning properties. Initially, the dataset pre-processing is performed, which eliminates the noise, unwanted data from EEG dataset. Then, the deep learning based customized convolution neural network (CCNN) is trained on pre-processed EEG data for precise extraction of disease correlated features. The machine learning based exhaustive random forest (ERF) feature selection is used to optimize features from the CCNN features, which are highly correlated with disease dependent properties. In conclusion, the recurrent neural network (RNN) based bi-directional long short-term memory (BLSTM) is used in order to detect brain seizures from chosen ERF features. Training and testing of suggested methodology had made use of CHB-MIT Scalp EEG Database. The aforementioned model has achieved the values of 98.36%, 97.54%, 97.91%, 98% and 95.08% respectively for precision, sensitivity, F1-Score, accuracy and specificity. The findings of the simulations demonstrate that the suggested BESD-Net led to superior performance when compared to the technologies that are already in use.

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Section: Literature Surveymentioning

confidence: 99%

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Prasanna,

Rahman,

Bayleyegn

2023

IEEE Access

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“…LSTM networks [15,16,17] have shown substantial promise in capturing temporal dependencies within EEG signals, making them well-suited for seizure detection and prediction tasks. Hezam et al [8] proposed a novel hybrid LSTM model that leverages both short-term and long-term EEG patterns to achieve enhanced detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast to other efforts that typically apply RNNs to the entire sequence LSTM networks [11,15,17], we reconstruct the initial long sequence into numerous shorter ones, each of which contains just local information and is processed by an ARNN cell. Specifically, we create a local window of size m of the sequence that encompasses m consecutive locations and forms l local windows.…”

Section: Arnn Cellmentioning

confidence: 99%

Deep Optimized Electrodes and Frequency Bands in the Phase Space for Identification of Seizures

Rukhsar

Tiwari

Panda

2022

2022 IEEE 19th India Council International Conference (INDICON)

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The electroencephalogram (EEG) has been extensively used to record brain signals for the analysis of neurological diseases over several decades. Epilepsy is the one of the most common neurological disorder in the world. It is associated with seizures, which are a clinical manifestation of abnormal, excessive excitation and synchronisation of a population of cortical neurons, resulting in abrupt changes in consciousness, movement, behaviour, and feelings. Our goal in this letter is to propose a computationally efficient EEG based system that can detect seizures with improved detection performance. To deeply optimize the electrodes and frequency bands, we proposed a fast computational algorithm by finding the critical electrodes and optimal sub-bands of EEG signals. We used wavelet packet decomposition (WPD) to find optimal sub-bands and maximum relevance minimum redundancy (mRMR) method for electrodes selection. It is found that the portrait of the features in the phase space depicted promising discriminating ability to detect seizure efficiently. Experimental results show that the proposed method outperformed the state-of-art methods and provided 100% and 94.79% of detection accuracy on UBonn and CHB-MIT datasets, respectively, at a significantly reduced computational cost.

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“…EEG signals are nonstationary, and therefore, time–frequency (TF) and time-scale representations are commonly used for their analysis [ 6 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. A time–frequency distribution (TFD) enables us to describe signal energy simultaneously in time and frequency.…”

Section: Introductionmentioning

confidence: 99%

Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis

Jurdana

Vrankić

Lopac

et al. 2023

Sensors

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Instantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike and oscillatory characteristics. Unlike previous methods that use IF alone, the proposed method utilizes information obtained from localized Rényi entropies (LREs) to generate a binary map that automatically identifies regions requiring a different estimation strategy. The method combines IF estimation algorithms for multicomponent signals with time and frequency support information to improve signal ridge estimation in the time–frequency distribution (TFD). Our experimental results indicate the superiority of the proposed combined IF and GD estimation approach over the IF estimation alone, without requiring any prior knowledge about the input signal. The LRE-based mean squared error and mean absolute error metrics showed improvements of up to 95.70% and 86.79%, respectively, for synthetic signals and up to 46.45% and 36.61% for real-life EEG seizure signals.

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Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

Cited by 36 publications

References 37 publications

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Brain Epileptic Seizure Detection Using Joint CNN and Exhaustive Feature Selection With RNN-BLSTM Classifier

Deep Optimized Electrodes and Frequency Bands in the Phase Space for Identification of Seizures

Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis

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