In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. Hence, machine learning techniques can be used to improve the accuracy of detection. The manual examination of these signals by experts is strenuous and time consuming. N2 - Electroencephalogram (EEG) is widely used to monitor the brain activities. T1 - A deep convolutional neural network model for automated identification of abnormal EEG signals Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.',

In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.Ībstract = 'Electroencephalogram (EEG) is widely used to monitor the brain activities.

Electroencephalogram (EEG) is widely used to monitor the brain activities.

Abnormal eeg