A novel framework based on biclustering for automatic epileptic seizure detection

Qin, Lin; Ye, Shuqun; Wu, Chunqing; Gu, Wencheng; Wang, Hua; Zhang, Huailing; Xue, Yun

doi:10.1007/s13042-017-0716-2

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…The accurate diagnosis for epilepsy can be done with the help of an effective detection method. A framework based on bi-clustering was proposed by Lin et al (2019) using ELM classifier. The main limitations of using bi-clustering algorithm were using random numbers for replacing discovered bi-clusters and masking of null values.…”

Section: Related Workmentioning

confidence: 99%

An automated epileptic seizure detection using optimized neural network from EEG signals

2023

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Among the central nervous system (neurological) disorders, epilepsy is considered to be a dangerous and chronic disorder that causes recurring seizures, showing unusual behaviour for some period of time, shaking of hands and legs, loss of sensation and awareness. It occurs when electrical activities in the brain become abnormal. The test performed in order to detect epileptic seizures is known as electroencephalograph (EEG) signal test which is conducted by analysing the electrical impulses in the brain. The manual identification process of EEG brain signals is time consuming and a laborious task. So the neurologists may sometimes give a varying result which may affect the performance of detecting epileptic seizures. The combination of manual identification of the signal and machine learning technique will be able to provide a correct result in a confusion situation reducing analytic and therapeutic errors that are unavoidable in human clinical practice. The existing approaches of detection of epileptic seizures cannot correctly train their network if the size of the dataset is very large thus obtaining lower classification accuracy. An optimized neural network technique is proposed in this work for automatic detection of Epileptic seizure using EEG signals of the brain. The EEG signals are used for feature extraction by applying Discrete Wavelet Transform (DWT). Using the extracted features, input samples are created and then fed to a hybrid model which is a combination of self-organizing neural network (SONN) and multilayer perceptron (MLP) trained with a genetic algorithm (GA). This model acts as a classifier for detecting Epileptic seizures by classifying the samples into two classes namely Non-Epileptic and Epileptic class. Clustering is performed first for the large dataset samples using SONN and for each cluster, an MLP-GA network is used to train the samples belonging to the same cluster. Performing clustering helps to correctly train the MLP networks for obtaining high classification accuracy. GA has been specifically chosen as a learning algorithm instead of Backpropagation (BP) algorithm for MLP because GA can find the global minima thus solving the local minima problem faced by BP algorithm. The performance measures are evaluated thereby achieving precision value of 98%, recall value of 100%, F1 score value of 98.99% and an overall accuracy of 99.2%. The proposed hybrid method using SONN and MLP-GA has more potential to classify the EEG signals.

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Section: Related Workmentioning

confidence: 99%

An automated epileptic seizure detection using optimized neural network from EEG signals

2023

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“…Research results [23] have shown that the average execution time of the BPNN is far more than that in other algorithms. Moreover, the conventional learning algorithms for ANN are prone to fall into a local minimum [23]. Therefore, the learning speed for conventional ANN is too slow to meet the requirements of seizure detection [23].…”

Section: Introductionmentioning

confidence: 98%

“…Artificial neural network (ANN) algorithms, such as the backpropagation (BP), convolutional neural network (CNN) and deep learning, have been widely used to classify epileptic EEG signals [18,19,20,21,22]. Although ANN has been found to exhibit good performance in seizure detection, it requires an extensive training process and a complicated design procedure since the connection weights and biases are timeconsuming to be adjusted especially for lots of hidden layers [17,22,23,24]. Research results [23] have shown that the average execution time of the BPNN is far more than that in other algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Although ANN has been found to exhibit good performance in seizure detection, it requires an extensive training process and a complicated design procedure since the connection weights and biases are timeconsuming to be adjusted especially for lots of hidden layers [17,22,23,24]. Research results [23] have shown that the average execution time of the BPNN is far more than that in other algorithms. Moreover, the conventional learning algorithms for ANN are prone to fall into a local minimum [23].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the conventional learning algorithms for ANN are prone to fall into a local minimum [23]. Therefore, the learning speed for conventional ANN is too slow to meet the requirements of seizure detection [23]. Thus, designing a system with quick in classification and response in the epilepsy detection is very important since the seizures occur suddenly in a few seconds.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

VLSI design of multiclass classification using sparse extreme learning machine for epilepsy and seizure detection

Wang

Zhou

Luo

et al. 2022

IEICE Electron. Express

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An automatic detection system for distinguishing healthy, ictal, and inter-ictal EEG signals is of importance in clinical practice. This paper presents a low-complexity three-class classification VLSI system for epilepsy and seizure detection. The designed system consists of a discrete wavelet transform (DWT)-based feature extraction module, a sparse extreme learning machine (SELM) training module, and a multiclass classifier module. A lifting structure of Daubechies order 4 wavelet is introduced in three-level DWT to save circuit area and speed up the computational time. The SELM which is a novel machine learning algorithm with low hardware complexity and highperformance is used for on-chip training. One-against-one multiclass non-linear SELM is designed for the first time due to its high classification accuracy. The designed system is implemented on an FPGA platform and evaluated using the publicly available epilepsy dataset. The experimental results demonstrate that the designed system achieves high accuracy with low-dimensional feature vectors.

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An ELM Based Regression Model for ECG Artifact Minimization from Single Channel EEG

Dora

Biswal

2018

Intelligent Data Engineering and Automated Learning – IDEAL 2018

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A novel framework based on biclustering for automatic epileptic seizure detection

Cited by 9 publications

References 40 publications

An automated epileptic seizure detection using optimized neural network from EEG signals

An automated epileptic seizure detection using optimized neural network from EEG signals

VLSI design of multiclass classification using sparse extreme learning machine for epilepsy and seizure detection

An ELM Based Regression Model for ECG Artifact Minimization from Single Channel EEG

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