Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

Abstract

Epilepsy is a group of neurological disorders characterized by epileptic seizures, wherein electroencephalogram (EEG) is one of the most common technologies used to diagnose, monitor, and manage patients with epilepsy. A large number of EEGs have been recorded in clinical applications, which leads to visual inspection of huge volumes of EEG not routinely possible. Hence, automated detection of epileptic seizure has become a goal of many researchers for a long time. A novel method is therefore proposed to construct a patient-specific detector based on spatial-temporal complexity analysis, involving two commonly used entropy-based complexity analysis methods, which are permutation entropy (PE) and sample entropy (SE). The performance of spatial-temporal complexity method is evaluated on a shared dataset. Results suggest that the proposed epilepsy detectors achieve promising performance: the average sensitivities of PE and SE in 23 patients are 99% and 96.6%, respectively. Moreover, both methods can accurately recognize almost all the seizure-free EEG. The proposed method not only obtains a high accuracy rate but also meets the real-time requirements for its application on seizure detection, which suggests that the proposed method has the potential of detecting epileptic seizures in real time.

Effects of Ageing and Sex on Complexity in the Human Sleep EEG: A Comparison of Three Symbolic Dynamic Analysis Methods

“…However, increasing the time delay is equivalent to downsampling the time series, which could lead to a loss of information by not following Nyquist criterion. Furthermore, the combination of embedded vector length and time delay also affects the maximum frequency range coverage (10) that can be achieved. The maximum frequency that can be measured in relation to the sampling frequency f s and input parameters m and is given by [64] = ( * ) .…”

“…In the current study, the SDA methods Lempel-Ziv complexity (LZC), permutation entropy (PE), and permutation Lempel-Ziv complexity (PLZC) were used. LZC was chosen because this technique was successful in characterising EEG signals in different conditions (e.g., epilepsy, Alzheimer's, and Parkinson's disease) [8][9][10][11][12][13][14][15]. LZC was introduced by Lempel and Ziv [16] and characterises complexity in Kolmogorov's sense [17] (i.e., the smallest binary program capable of reproducing an information containing sequence).…”

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

“…The most common technology to detect various kinds of brain activity, both normal and pathological, is based on EEG recordings [25,26], although recently Wu et al [27] introduced a new approach for MEG data classification using a support vector machine (SVM) with a radial basis kernel function, which was shown to be an effective method for right and left temporal lobe epilepsy recognition. In the present paper, we analyze different types of ANNs in order to reveal most convenient configurations.…”

“…Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs.…”

Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

“…Since then, it has been extensively applied in various fields. Without being exhaustive, applications such as fault diagnosis [12,13], biomedical signal processing [6,[14][15][16], and stock market analysis [17,18] can be enumerated. Despite considerable success, there are still defects in PE, which have motivated researchers to present modifications for the original algorithm.…”