Abstract
Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%.
Highlights
Epilepsy is the most common severe neurological disorder, and nearly 50 million people are diagnosed with epilepsy worldwide [1]
The Epileptic detection performance of the proposed deep convolutional long short-term memory (C-LSTM) model was evaluated by comparing the overall accuracy, F1-score, and sensitivity
For avoiding the overfitting problem of the neural network method, all of the experiments are run more than 20 times
Summary
Epilepsy is the most common severe neurological disorder, and nearly 50 million people are diagnosed with epilepsy worldwide [1]. The challenging is locating the ictal spikes and seizures during EEG recording It is a time-consuming process for an expert to analyze the entire length of the EEG recordings, in order to detect epileptic activity [10]. Many methodologies are proposed to detect epileptic seizures and tumors in the brain based on EEG signals. These presented classifiers have achieved perfect classification accuracy, some drawbacks limit their performance. Most of the previous works only consider binary classification problems because it is difficult to establish a multiple-class model based on the EEG signals, which might decrease the accuracy.
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