Automated Detection of Epileptic Seizures Using Wavelet Entropy Feature with Recurrent Neural Network Classifier

Kumar, S. Pravin; Sriraam, N.; Benakop, Prabhu

doi:10.1109/tencon.2008.4766836

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…Johnrose et al 23 proposed a novel method using the rag-Rider optimization algorithm (rag-ROA) and deep recurrent neural network (Deep RNN) for EEG seizure detection. Kumar et al 24 focused on discovering epileptic seizures automatically, which introduces a method utilizing wavelet, sample, and spectral entropy features extracted from EEG signals. Huang et al 25 introduced an end-to-end deep neural network, attention-based CNN-BiRNN that uses multi-scale convolution, attention models, and multi-stream bidirectional recurrent models to automatically detect seizures with high sensitivity and specificity.…”

Section: Related Workmentioning

confidence: 99%

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Jibon,

Jamil Chowdhury,

Miraz

et al. 2024

DIGITAL HEALTH

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Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.

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Section: Related Workmentioning

confidence: 99%

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Jibon,

Jamil Chowdhury,

Miraz

et al. 2024

DIGITAL HEALTH

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“…where C are the coefficients of detail and approximation at different levels . In our study wavelet packet algorithm is implemented by using Haar, rbio3.1 sym7 and dmey for different levels of decompositions (Kumar et al 2008, Deng Wang et al 2010.…”

Section: Figure 3 Wavelet Packet Tree For 3 Levels Of Wavelet Packet ...mentioning

confidence: 99%

Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals

Raghu

Kumar

2015

International Journal of Biomedical and Clinical Engineering

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The scaling behavior of human electroencephalogram (EEG) signals is well exploited by appropriate extraction of time – frequency domain and entropy based features. Such measurable inherently helps understanding the neurophysiological phenomenon of brain as well as its associated cortical activities. Being a non-linear time series, EEG's are assumed to be fragment of fluctuations. Several attempts have been made to study the EEG signals for clinical applications such as epileptic seizure detection, evoked response potential recognition, tumor detection, identification of alcoholics and so on. In all such applications appropriate selection of feature parameter plays an important role in discriminating normal EEG from abnormal. In the recent past one can find the importance of wavelet and wavelet packet towards EEG analysis. This proposed research work investigates the effect of wavelet packet log energy entropy on EEG signals. Entropy being the measure of relative information, the proposed study attempts to discriminate the normal EEGs from abnormal EEG's by employing the log energy entropy features. For better brevity, this study restricts to the analysis of epileptic seizure from normal EEGs. Different decomposition levels from 2 to 5 were considered for wavelet packets with application of Haar, rbio3.1, sym7, dmey wavelets. A one second windowing was introduced for the data segmentation and Shannon's log energy entropy was estimated. Then the statistical non-parametric Wilcoxon model was employed. The result shows that the application of wavelet packet log energy entropy found to be a potential indicator for discriminating epileptic seizure from normal.

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The Utilisation of Dynamic Neural Networks for Medical Data Classifications- Survey with Case Study

Hussain

Fergus

Al‐Jumeily

et al. 2015

Lecture Notes in Computer Science

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Automated Detection of Epileptic Seizures Using Wavelet Entropy Feature with Recurrent Neural Network Classifier

Cited by 11 publications

References 13 publications

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signal

Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals

The Utilisation of Dynamic Neural Networks for Medical Data Classifications- Survey with Case Study

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