Classification of EEG signals for detection of epileptic seizure activities based on feature extraction from brain maps using image processing algorithms

Jothiraj, Sairamya Nanjappan; George, S. Thomas; Ramasamy, Balakrishnan; Deivendran, Narain Ponraj; M.S.P, Subathra

doi:10.1049/iet-ipr.2018.5418

Cited by 11 publications

(6 citation statements)

References 32 publications

(39 reference statements)

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“…The public available Bonn University database was used in this proposed research work to detect the seizure, inter-ictal, and healthy subjects with the help of EEG signals [36]. The dataset was composed of five subsets (A-E), each containing 100 single channel EEG segments with a length of 23.6 s. The sampling frequency of the epilepsy dataset is 173.61 Hz with a 12-bit resolution such that each segment is composed of 4096 points.…”

Section: Eeg Datasetmentioning

confidence: 99%

“…Still, it increases the hardware's complexity with complicated feature calculation circuits [11][12][13][14][15][16][17][18].To address the shortcomings of these hardware implementations, on-chip linear SVM classifier, polyphase based DWT, and hardware-friendly features are selected to achieve better results in seizure detection. The proposed algorithms are implemented on the FPGA device and were tested using a freely available Bonn university dataset [36].…”

Section: Introductionmentioning

confidence: 99%

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Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier

Shanmugam¹,

Selvathi²

2021

IET Circuits, Devices & Syst

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Epilepsy is one of the most common neurological disorders; it affects millions of people globally. Because of the risks to health that it causes, the study and analysis of epilepsy have been given considerable attention in the biomedical field. In a neurological diagnosis, an automated device for detecting seizures or epilepsy from an electroencephalogram (EEG) signal has a significant role. This research work proposes a very large scale integration implementation system for the automatic detection of seizures. Before classification, feature extraction was performed by discrete wavelet transform (DWT) and on-chip classification was performed by a linear support vector machine. The polyphase architecture of Daubechies fourth-order wavelet three-level DWT was used to minimize computational time. The systolic array architecture-based support vector machine classifier using parallel processing helps to minimize the computational complexity of the proposed method. This research work uses an open access EEG dataset. Hardware implementation was done on a field-programmable gate array (FPGA). Efficient results were produced compared with the existing system on chip (SoC) and FPGA seizure detection systems.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Eeg Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier

Shanmugam¹,

Selvathi²

2021

IET Circuits, Devices & Syst

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“…Feature extraction is an essential process for event classification of signals and images in the medical field for clinical diagnosis [53]. In this study, five nonlinear entropy-based discriminating features from the decomposed Hadamard coefficient of EEG signal were extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Prasanna

Subathra

Mohammed

et al. 2020

Sensors

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The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

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“…Study on the brain structure and its related research appeared in particular cognitive function for nearly 10 years, with high performance due to the rapid development of computer technology and to the widespread use of noninvasive brain imaging technology, to become one of the difficult and frontier hot academic research directions. However, understanding and recognizing the neural mechanism behind the mysterious brain is still a basic and difficult problem in current brain science research [1][2][3]. Among these complicated and challenging problems in brain science, brain plasticity is one of the most important issues.…”

Section: Introductionmentioning

confidence: 99%

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training

Huang

Cheng

Liu

2022

Computational Intelligence and Neuroscience

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Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.

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Classification of EEG signals for detection of epileptic seizure activities based on feature extraction from brain maps using image processing algorithms

Cited by 11 publications

References 32 publications

Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier

Very large scale integration implementation of seizure detection system with on‐chip support vector machine classifier

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training

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