DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers

Sharmila, A.; Geethanjali, P.

doi:10.1109/access.2016.2585661

Cited by 261 publications

(108 citation statements)

References 45 publications

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“…M5 with augmentation scheme 1, we present and discuss the results for different experiment cases related to epilepsy detection. We considered three experiment cases: (i) normal vs interictal vs ictal (AB vs CD vs E), (ii) normal vs epileptic (AB vs CDE and AB vs CD), (iii (Sharmila et al, 2016). The remaining 2 experiments have rarely or never been tested.…”

Section: Resultsmentioning

confidence: 99%

“…The method proposed in (Sharmila et al, 2016) employ discrete wavelet transform (DWT) for feature extraction and nonlinear classifiers i.e. naïve Bayes (NB) and k-nearest neighbor (k-NN) classifier for the classification of epileptic and non-epileptic signals.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…For generating many instances from one record, we adopted the sliding window approach similar to the one presented in refs. (Sharmila et al, 2016;T. Zhang et al, 2017).…”

Section: Dataset and Data Augmentation Schemesmentioning

confidence: 99%

“…normal vs. ictal vs. inter-ictal), ii) less available labeled data, and ii) low accuracy. To help and aid neurologists, we need a generalized automatic system that can show good performance even with fewer training samples (Andrzejak et al, 2001;Sharmila et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An automated system for epilepsy detection using EEG brain signals based on deep learning approach

Ullah

Hussain

Qazi

et al. 2018

Expert Systems with Applications

469

213

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Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97±1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1±0.9% on the University of Bonn dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…For generating many instances from one record, we adopted the sliding window approach similar to the one presented in refs. (Sharmila et al, 2016;T. Zhang et al, 2017).…”

Section: Dataset and Data Augmentation Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An automated system for epilepsy detection using EEG brain signals based on deep learning approach

Ullah

Hussain

Qazi

et al. 2018

Expert Systems with Applications

469

213

View full text Add to dashboard Cite

show abstract

“…Such automated seizure detectors can trigger alarm when users are or will possibly be in a state of seizure. So far, algorithms for automated epileptic seizure detection proposed in most studies consist of three parts: (1) signal domain transformation, such as frequency domain via Fourier transform [3], wavelet time-frequency domain via discrete wavelet transform (DWT) [4,5], weighted and specific shapes via Hermite transformation [6] or original domain without transformation [7]; (2) feature extraction in the target domain, such as energy features [8] and complexity features [9]; and (3) machine learning based classification using a support vector machine (SVM) [10], k-nearest neighbor (KNN) [11] or artificial neural network (ANN) [12]. However, all the aforementioned three parts have shown limitations in some application scenarios, which are discussed in the following paragraphs separately.…”

Section: Introductionmentioning

confidence: 99%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.

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Machine Learning Techniques for IoT Data Analytics

Afshan

Rout

2021

Big Data Analytics for Internet of Things

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DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers

Cited by 261 publications

References 45 publications

An automated system for epilepsy detection using EEG brain signals based on deep learning approach

An automated system for epilepsy detection using EEG brain signals based on deep learning approach

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Machine Learning Techniques for IoT Data Analytics

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