Epileptic Seizure Detection of EEG Signal Using Wavelet Based Feature Extraction and Machine Learning Algorithms
05 Mar 2021-pp 1393-1401
TL;DR: In this article, the EEG signal from epileptic and healthy patients were divided and broken into frequency sub-band with the help of Discrete Wavelet Transform (DWT) and extracted 10 features out of them.
Abstract: Epilepsy is a common chronic nervous disorder affecting fifty million individuals worldwide. Epileptic Seizures are the results of the fleeting and surprising electric phenomenon of the brain. Electroencephalogram (EEG) signal is a vital information supply in diagnosing epilepsy because it records electrical and neural activities from the brain. Traditionally, these graphical record signals (EEG) are manually observed by medical practitioners which is time taking and gives improper result. In this paper, we have a tendency to propose a brand-new computer based mostly automatic epilepsy seizure detection concept. The EEG signal from epileptic and healthy patients were divided and broken into frequency sub-band with the help of Discrete Wavelet Transform (DWT). We applied feature extraction in these frequency sub-bands and extracted 10 features out of them. The features extracted were then fed into four different classifiers particularly, Support Vector Machine (SVM) k-nearest neighbor (kNN), Naive-Bayes and Decision Tree classifiers. Six parameters are further used to compare the performance of these classifiers. An accuracy of 99.33% has been achieved in our work.
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TL;DR: In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
1,117 citations
TL;DR: In this article, an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models is proposed to detect ternary cases e.g. normal vs. interictal.
Abstract: Epilepsy is a life-threatening and challenging neurological disorder, which is affecting a large number of people all over the world. For its detection, encephalography (EEG) is a commonly used clinical approach, but manual inspection of EEG brain signals is a time-consuming and laborious process, which puts a heavy burden on neurologists and affects their performance. Several automatic systems have been proposed using traditional approaches to assist neurologists, which perform well in detecting binary epilepsy scenarios e.g. normal vs. ictal, but their performance degrades in classifying ternary case e.g. ictal vs. normal vs. inter-ictal. To overcome this problem, we propose a system that is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. Though a CNN model learns the internal structure of data and outperforms hand-engineered techniques, the main issue is the large number of learnable parameters, whose learning requires a huge volume of data. To overcome this issue, P-1D-CNN works on the concept of refinement approach and it involves 61% fewer parameters compared to standard CNN models and as such it has better generalization. Further to overcome the limitations of the small amount of data, we propose two augmentation schemes. We tested the system on the University of Bonn dataset, a benchmark dataset; in almost all the cases concerning epilepsy detection, it gives an accuracy of 99.1 ± 0.9% and outperforms the state-of-the-art systems. In addition, while enjoying the strength of a CNN model, P-1D-CNN model requires 61% less memory space and its detection time is very short (
352 citations
TL;DR: The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection and has been compared with existing methods for the classification of the aforementioned problems.
Abstract: The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.
202 citations
25 Apr 2017
TL;DR: The proposed algorithm is effective in seizure onset detection with 96% sensitivity, 0.1 per hour median false detection rate, and 1.89 s average detection latency, indicating potential usage in real-time applications.
Abstract: This paper proposes a novel patient-specific real-time automatic epileptic seizure onset detection, using both scalp and intracranial electroencephalogram (EEG). The proposed technique obtains harmonic multiresolution and self-similarity-based fractal features from EEG for robust seizure onset detection. A fast wavelet decomposition method, known as harmonic wavelet packet transform (HWPT), is computed based on Fourier transform to achieve higher frequency resolutions without recursive calculations. Similarly, fractal dimension (FD) estimates are obtained to capture self-similar repetitive patterns in the EEG signal. Both FD and HWPT energy features across all EEG channels at each epoch are organized following the spatial information due to electrode placement on the skull. The final feature vector combines feature configurations of each epoch within the specified moving window to reflect the temporal information of EEG. Finally, relevance vector machine is used to classify the feature vectors due to its efficiency in classifying sparse, yet high-dimensional data sets. The algorithm is evaluated using two publicly available long-term scalp EEG (data set A) and short-term intracranial and scalp EEG (data set B) databases. The proposed algorithm is effective in seizure onset detection with 96% sensitivity, 0.1 per hour median false detection rate, and 1.89 s average detection latency, respectively. Results obtained from analyzing the short-term data offer 99.8% classification accuracy. These results demonstrate that the proposed method is effective with both short- and long-term EEG signal analyzes recorded with either scalp or intracranial modes, respectively. Finally, the use of less computationally intensive feature extraction techniques enables faster seizure onset detection when compared with similar techniques in the literature, indicating potential usage in real-time applications.
167 citations
TL;DR: The clinical feasibility of the proposed seizure detection approach achieving superior performance over the cutting-edge techniques in terms of seizure detection performance and robustness is demonstrated.
136 citations