Key result
A novel deep one-dimensional convolutional neural network (1D CNN) model for detecting epilepsy-related EEG abnormalities achieved an accuracy of 85.48% and an error rate of 14.5%.
Why the study?
Visual inspection of EEGs to detect epileptogenic abnormalities is time-consuming, limited by artifacts, variable by reader experience, and ineffective at detecting irregularities.
Does a 1D CNN model accurately detect abnormalities in epilepsy EEGs?
Does a 1D CNN model accurately detect abnormalities in epilepsy EEGs?
A novel 1D CNN model can automatically detect epilepsy-related abnormalities in EEG signals with 85.48% accuracy, potentially reducing the need for time-consuming visual inspection.
Not ready for clinical EEG use; leaves open prospective validation of 1D CNN models in epilepsy.
Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain's function. EEG is an effective instrument for deciphering the brain's complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time-consuming, restricted by technical artifacts, produces varying findings depending on the reader's level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, EEG signals are analyzed using a convolutional neural network (CNN). For the automatic detection of abnormal and normal EEG indications, a novel deep one-dimensional convolutional neural network (1D CNN) model is suggested in this paper. The regular, pre-ictal, and seizure categories are detected using this approach. The proposed model achieves an accuracy of 85.48% and a reduced categorization error rate of 14.5%.
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Syamsundararao et al. (2022) studied Epilepsy. One-dimensional convolutional neural network (1D CNN) model was evaluated on Automatic detection of regular, pre-ictal, and seizure categories in EEG signals. A novel deep one-dimensional convolutional neural network (1D CNN) model for detecting epilepsy-related EEG abnormalities achieved an accuracy of 85.48% and an error rate of 14.5%.
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