Epileptic EEG Signal Classification using Multi-class Convolutional Neural Network

Ramakrishnan, S.; Murugavel, A. S. Muthanantha; Saravanan, P.

doi:10.1109/vitecon.2019.8899453

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…The recordings are grouped into 23 cases and collected by 22 individuals (5 males, aged 3-22 years; and 17 females, aged 1. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Data Descriptionmentioning

confidence: 99%

EEG Seizure Detection using Convolutional Neural Network With Grad Cam

Joseph,

Nair,

Menacherry

et al. 2024

Preprint

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The project’s main goal is to employ deep learning techniques to analyze EEG readings and detect seizure episodes. EEG data is first preprocessed and split into sets for training and validation. Next, a Convolutional Neural Network (CNN) model with several convolutional and dense layers is built. Using an Adam optimizer, binary cross-entropy loss is optimized as part of the model’s training process. Furthermore, classification reports and accuracy metrics are used to assess the performance of the model. Weight extraction from the first convolutional layer is used to visualize feature importance for interpretability. Grad-CAM heatmaps are produced to identify the EEG signal regions that are most important for seizure prediction. This method helps detect pertinent EEG patterns linked to seizures and comprehend the model’s decision-making process. All things considered, the experiment shows how well deep learning works for EEG-based seizure identification and offers insights into the significance of features using Grad-CAM visualization approaches. The method has potential for real-time seizure monitoring and may aid in the creation of assistive devices for the treatment of epilepsy.

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“…The recordings are grouped into 23 cases and collected by 22 individuals (5 males, aged 3-22 years; and 17 females, aged 1. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Data Descriptionmentioning

confidence: 99%

EEG Seizure Detection using Convolutional Neural Network With Grad Cam

Joseph,

Nair,

Menacherry

et al. 2024

Preprint

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“…Although it is well-known that deep learning model CNN considered to be a time-consuming solution, it is not clear for this paper how good performance can be obtained from the classifier. Ramakrishnan et al [19] have focused on detecting patients with epileptic seizure based on Convolutional Neural Network (CNN). This paper has tested both time domain and frequency EEG features and their impact on CNN.…”

Section: Convolution Neural Network (Cnn) For Eeg Classificationmentioning

confidence: 99%

“…Table 2 shows resultant accuracy by Artificial Neural Network (ANN) classification for [5,[13][14][15][16][17]. Table 3 shows resultant accuracy by Convolution Neural Network (CNN) classification for [4,[18][19][20]. Table 4 shows resultant accuracy by K-Nearest Neighbor (K-NN) classification for [7,21,22].…”

Section: Analysis and Evaluationmentioning

confidence: 99%

Survey for Electroencephalography EEG Signal Classification Approaches

Al-Fraiji

Al‐Shammary

2021

Mobile Computing and Sustainable Informatics

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This paper presents a literature survey for electroencephalogram (EEG) signal classification approaches based on machine learning algorithms. EEG classification plays a vital role in many health applications using machine learning algorithms. Mainly, they group and classify patient signals based on learning and developing specific features and metrics. In this paper, 32 highly reputed research publications are presented focusing on the designed and implemented approach, applied dataset, their obtained results and applied evaluation. Furthermore, a critical analysis and statement are provided for the surveyed papers and an overall analysis in order to have all the papers under an evaluation comparison. SVM, ANN, KNN, CNN, LDA, multi-classifier and more other classification approaches are analyzed and investigated. All classification approaches have shown potential accuracy in classifying EEG signals. Evidently, ANN has shown higher persistency and performance than all other models with 97.6% average accuracy.

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