Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure

Mir, Waseem Ahmad; Anjum, Mohd; Izharuddin,; Shahab, Sana

doi:10.3390/diagnostics13040773

Cited by 31 publications

(17 citation statements)

References 42 publications

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“…By applying the same algorithm, 96.67% accuracy for ictal and interictal classification (Task-1) on Bonn dataset was achieved. When compared to the studies in [16,21], a higher accuracy was achieved in the proposed study for Task-2. Accuracy of 80.00% and 96.00% for Task-2 were obtained in [20,24] on Bonn dataset, respectively, while accuracy of 96.10% was obtained in AU dataset for Task-2, which is higher than the literature results.…”

Section: Resultsmentioning

confidence: 67%

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Identification of TLE Focus from EEG Signals by Using Deep Learning Approach

et al. 2023

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Temporal lobe epilepsy, a neurological disease that causes seizures as a result of excessive neural activities in the brain, is the most common type of focal seizure, accounting for 30–35% of all epilepsies. Detection of epilepsy and localization of epileptic focus are essential for treatment planning and epilepsy surgery. Currently, epileptic focus is decided by expert physician by examining the EEG records and determining EEG channel where epileptic patterns begins and continues intensely during seizure. Examination of long EEG recordings is very time-consuming process, requires attention and decision can vary depending on physician. In this study, to assist physicians in detecting epileptic focus side from EEG recordings, a novel deep learning-based computer-aided diagnosis system is presented. In the proposed framework, ictal epochs are detected using long short-term memory network fed with EEG subband features obtained by discrete wavelet transform, and then, epileptic focus identification is realized by using asymmetry score. This algorithm was tested on EEG database obtained from the Ankara University hospital. Experimental results showed ictal and interictal epochs were classified with accuracy of 86.84%, sensitivity of 86.96% and specificity of 89.68% on Ankara University hospital dataset, and 96.67% success rate was obtained on Bonn EEG dataset. In addition, epileptic focus was identified with accuracy of 96.10%, sensitivity of 100% and specificity of 93.80% by using the proposed deep learning-based algorithm and university hospital dataset. These results showed that proposed method can be used properly in clinical applications, epilepsy treatment and surgical planning as a medical decision support system.

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Section: Resultsmentioning

confidence: 67%

“…In the literature, some approaches exist about epilepsy detection from EEG signals. Current studies mainly focused on classifying ictal and interictal epochs by using various machine learning [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] and deep learning methods [16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Identification of TLE Focus from EEG Signals by Using Deep Learning Approach

et al. 2023

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“…They achieved a maximum accuracy of 98.67%. Similarly, another recent study [24] employed a deep convolutional autoencoder and bidirectional long short memory for epileptic seizure detection (DCAE-ESD-Bi-LSTM) for the same task, and achieved more accurate (99.8%) and optimized results (99.9% precision and 99.6% F1 score). A sparse autoencoder with a swarm-based DL method known as (SASDL) employing PSO, was proposed, which achieved an accuracy [25] of 98.5%.…”

Section: Dl-based Approachesmentioning

confidence: 96%

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Alshayeji

2023

Mach. Learn.: Sci. Technol.

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Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person's way of life. Manual diagnosis is a laborious and time-consuming task prone to human error; therefore, automating this task by developing an intelligent system is necessary. Existing deep learning (DL) models require high training time, large datasets, and machines with more memory and processing power. In addition, owing to the black-box nature of DL models, no one can determine the features that the network prefers for classification decisions. To overcome these challenges, this paper proposes an accurate, automatic, and fast intelligent system for epilepsy detection using a computer-aided diagnosis (CAD)-2D machine learning (ML) framework. Existing ML models struggle to produce reliable and acceptable diagnostic results owing to the low amplitude and nonstationary nature of electroencephalograms (EEG), particularly in clinical situations where environmental influences are almost impossible to eliminate. The proposed model was built on the CHB-MIT dataset, and it represents the first study that employs the speeded-up robust feature (SURF) bag of features (BOF) technique for this application, which generates local features from the spectrogram images of the respective 1D EEG signal inputs. In addition, DL features were extracted from the spectrogram images for model performance comparisons. Both features were used separately to train the ML classifiers. Implementing SURF offers fast computation and makes the model invariant to distortions, noise, scaling, and so on. Therefore, the proposed model is more suitable for real-time applications, proposed ML framework provides an enhanced accuracy of 99.78% from the SVM-RBF classifier, along with 99.56% sensitivity, 100% specificity, and an error rate of 0.22%. The higher detection accuracy demonstrated the effectiveness of the proposed framework for medical disease diagnosis applications.

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“…Though MEG has a few advantages over EEG due to its higher sensitivity to superficial cortical activity [40], capturing MEG signals requires expensive apparatus and a magnetically shielded room, making it less feasible than EEG. EEG analysis has been used as an approach to successfully detect many other neurological conditions such as Alzheimer's [41,42], Epilepsy [43][44][45][46], Brain Stroke [47], Schizophrenia [48][49][50], Depression [51,52], Insomnia [53], Autism [54] and Attention Deficit Hyperactivity Disorder (ADHD) [55,56]. From the previous studies, it is reasonably evident that EEG captures the minute nuances and salient features of the brain that can lead to the detection of neurological disorders.…”

Section: Related Workmentioning

confidence: 99%

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

Lal,

Chikkankod,

Longo

2023

Preprint

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Parkinson's Disease (PD) is an incurable neurological disorder that degenerates the cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) signal analysis can provide reliable information regarding PD conditions. However, EEG is a complex, multi-channel, and non-linear signal with noise that problematizes identifying PD symptoms. A few studies have employed Fractal Dimension (FD) to extract distinguishing PD features from EEG signals. However, no exploratory study exists, as per our knowledge, on the efficiency of the different FD measures. We aim to conduct a comparative analysis of the various FDs that, as feature extraction measures, can discriminate PD patients who are ON and OFF medication from healthy controls using ML architecture. This study has implemented and analyzed several techniques for segmentation, feature extraction, and ML models. The results show that K-Nearest Neighbors (KNN) classifier with Higuchi FD and 90% overlap for segmented window delivers the highest accuracies, yielding a mean accuracy of 99.65% for PD patients ON medication and 99.45% for PD patients OFF medication, respectively. The model accurately identifies the signs of the disease in resting-state EEG with almost equivalent accuracy in both OFF and ON medication patients. To enhance the interpretability in our study, we leveraged XGB's feature importance to generate brain topographic plots. This integration of Explainable AI (XAI) enhanced the transparency and comprehensibility of our model's classifications. Additionally, a comparison between the performance of FD and a few Entropy measures has also been drawn to validate the significance of FD as a superior feature extraction measure. This study contributes to the body of knowledge with an architectural pipeline for detecting PD in resting-state EEG while emphasizing Fractal Dimension as an effective way of extracting salient features from EEG signals.

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Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure

Cited by 31 publications

References 42 publications

Identification of TLE Focus from EEG Signals by Using Deep Learning Approach

Identification of TLE Focus from EEG Signals by Using Deep Learning Approach

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

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