Dimensionality Reduction Techniques for Processing Epileptic Encephalographic Signals

Harikumar, R.; Kumar, P.Sunil

doi:10.13005/bpj/587

Cited by 18 publications

(4 citation statements)

References 4 publications

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“…In many machine learning applications, such as data classification [1,2], face recognition [3,4], signal processing [5,6], and text categorization [7,8], the input data is usually high-dimensional which makes the calculations too complex, as well as requiring more computational time. In order to boost the performance and computational efficiency, various dimensionality reduction (DR) techniques have been proposed to preprocess these high-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Weighted Neighborhood Preserving Ensemble Embedding

2019

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Neighborhood preserving embedding (NPE) is a classical and very promising supervised dimensional reduction (DR) technique based on a linear graph, which preserves the local neighborhood relations of the data points. However, NPE uses the K nearest neighbor (KNN) criteria for constructing an adjacent graph which makes it more sensitive to neighborhood size. In this article, we propose a novel DR method called weighted neighborhood preserving ensemble embedding (WNPEE). Unlike NPE, the proposed WNPEE constructs an ensemble of adjacent graphs with the number of nearest neighbors varying. With this graph ensemble building, WNPEE can obtain the low-dimensional projections with optimal embedded graph pursuing in a joint optimization manner. WNPEE can be applied in many machine learning fields, such as object recognition, data classification, signal processing, text categorization, and various deep learning tasks. Extensive experiments on Olivetti Research Laboratory (ORL), Georgia Tech, Carnegie Mellon University-Pose and Illumination Images (CMU PIE) and Yale, four face databases demonstrate that WNPEE achieves a competitive and better recognition rate than NPE and other comparative DR methods. Additionally, the proposed WNPEE achieves much lower sensitivity to the neighborhood size parameter as compared to the traditional NPE method while preserving more of the local manifold structure of the high-dimensional data.

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Section: Introductionmentioning

confidence: 99%

Weighted Neighborhood Preserving Ensemble Embedding

2019

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“…So, it is important to reduce the dimensions of the EEG recorded data, and then, this data can be provided as an input to the classifiers for the classification of epilepsy risk levels from EEG signals. Several dimensionality techniques for the processing of the epileptic EEG data have been discussed in Harikumar and Kumar [8]. Dimensionality reduction is achieved by the projection of the data into a lower dimensional space.…”

Section: Introductionmentioning

confidence: 99%

“…By selecting the appropriate channels in a given data, dimensionality can be easily reduced [9]. By the projection of all the EEG data into a particular time domain signal which has a single dimension, the dimension of the data is easily reduced [8]. After reducing the dimension of the EEG data, it has to be classified to assess the epileptic risk levels.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Epilepsy Classification Using Techniques Such as Singular Value Decomposition, Approximate Entropy, and Weighted K-Nearest Neighbors Measures

P¹,

Rajaguru²

2016

Asian J Pharm Clin Res

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Objective:The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it using various post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activities of the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is used by both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis of many neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalent neurological disorders is epilepsy and it is easily characterized by recurrent seizures. Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent Component Analysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF). The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and Weighted KNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion:Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was when LDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risk levels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types of classifiers for the perfect classification of epilepsy risk levels from EEG signals.

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“…Each signal is very unique in an EEG and hence it is not repeatable. Also, based on the characteristics of the equipment or source, EEG signals are often noisy [4]. The observed dataset is focused primarily by the dimensionality reduction techniques and it avoids the generalization performance.…”

Section: Introductionmentioning

confidence: 99%

A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks

Rajaguru¹,

Prabhakar²

2016

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Characterized by recurrent and rapid seizures, epilepsy is a great threat to the livelihood of the human beings. Abnormal transient behaviour of neurons in the cortical regions of the brain leads to a seizure which characterizes epilepsy. The physical and mental activities of the patient are totally dampened with this epileptic seizure. A significant clinical tool for the study, analysis and diagnosis of the epilepsy is electroencephalogram (EEG). To detect such seizures, EEG signals aids greatly to the clinical experts and it is used as an important tool for the analysis of brain disorders, especially epilepsy. In this paper, the high dimensional EEG data are reduced to a low dimension by incorporating techniques such as Fuzzy Mutual Information (FMI), Independent Component Analysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and Variational Bayesian Matrix Factorization (VBMF). After employing them as dimensionality reduction techniques, the Neural Networks (NN) such as Cascaded Feed Forward Neural Network (CFFNN), Time Delay Neural Network (TDNN) and Generalized Regression Neural Network (GRNN) are used as Post Classifiers for the Classification of Epilepsy Risk Levels from EEG signals. The bench mark parameters used here are Performance Index (PI), Quality Values (QV), Time Delay, Accuracy, Specificity and Sensitivity.

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Dimensionality Reduction Techniques for Processing Epileptic Encephalographic Signals

Cited by 18 publications

References 4 publications

Weighted Neighborhood Preserving Ensemble Embedding

Weighted Neighborhood Preserving Ensemble Embedding

Assessment of Epilepsy Classification Using Techniques Such as Singular Value Decomposition, Approximate Entropy, and Weighted K-Nearest Neighbors Measures

A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks

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