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Sandeep Kumar Satapathy

Bio: Sandeep Kumar Satapathy is an academic researcher from VIT University. The author has contributed to research in topics: Support vector machine & Population. The author has an hindex of 6, co-authored 26 publications receiving 233 citations. Previous affiliations of Sandeep Kumar Satapathy include K L University & Siksha O Anusandhan University.

Papers
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Journal ArticleDOI
TL;DR: A technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoen coder along with the image decomposition property of wavelet transform is proposed and it is noted that the proposed method outshines the existing methods.
Abstract: Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods.

183 citations

Journal ArticleDOI
TL;DR: To classify the EEG signal, a radial basis function neural network (RBFNN) is used, which can be trained to optimize the mean square error (MSE) by using a modified particle swarm optimization (PSO) algorithm.

64 citations

Journal ArticleDOI
TL;DR: The epileptic disorder in human brain through EEG signal analysis is analyzed by integrating the best attributes of Artificial Bee Colony and radial basis function networks by using Discrete Wavelet Transform (DWT) technique for extraction of potential features from the signal.

40 citations

Journal ArticleDOI
TL;DR: A classification model using hybrid deep belief networks (DBN) to classify magnetic resonance imaging (MRI) for GBM tumor and statistical validation verifies that the combination of DTW and DBN outperformed the other classifiers in terms of training time, space complexity and classification accuracy.
Abstract: Glioblastoma (GBM) is a stage 4 malignant tumor in which a large portion of tumor cells are reproducing and dividing at any moment. These tumors are life threatening and may result in partial or complete mental and physical disability. In this study, we have proposed a classification model using hybrid deep belief networks (DBN) to classify magnetic resonance imaging (MRI) for GBM tumor. DBN is composed of stacked restricted Boltzmann machines (RBM). DBN often requires a large number of hidden layers that consists of large number of neurons to learn the best features from the raw image data. Hence, computational and space complexity is high and requires a lot of training time. The proposed approach combines DTW with DBN to improve the efficiency of existing DBN model. The results are validated using several statistical parameters. Statistical validation verifies that the combination of DTW and DBN outperformed the other classifiers in terms of training time, space complexity and classification accuracy.

18 citations

Journal Article
TL;DR: The analysis confirms with the present setting that, recurrent neural network performs poor in all the cases of prepared epilepsy data, however, SVM and probabilistic neural networks are quite effective and competitive.
Abstract: Electroencephalogram (EEG) signal is a modest measure of electric flow in a human brain. It is responsible for information flow through the neurons in the brain which controls and monitors the full torso. Hence, to widening and in-depth understanding of effectiveness in EEG signal analysis is the primary focus of this paper. Moreover, machine learning techniques often proven as more efficacious compared to other techniques. To this effect, the present study primarily focuses on the analysis of EEG signal through the classification of the processed data by discrete wavelet transform (DWT) for identification of epileptic seizures using machine learning techniques. Machine learning techniques like neural networks and support vector machine (SVM) are the focus of this paper for classification of EEG signals to label epilepsy patients. In neural networks, the empirical analysis gives focus on multi-layer perceptron, probabilistic neural network, radial basis function neural networks, and recurrent neural networks. Further, for multi-layer neural networks different propagation training algorithms are examined such as BackPropagation, Resilient-Propagation, and Quick-Propagation. For SVM, several kernel methods are considered such as Linear, Polynomial, and RBF for empirical analysis. The analysis confirms with the present setting that, recurrent neural network performs poor in all the cases of prepared epilepsy data. However, SVM and probabilistic neural networks are quite effective and competitive.

15 citations


Cited by
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01 Jan 2002

9,314 citations

01 Jan 1991
TL;DR: In this article, a new statistic called approximate entropy (ApEn) was developed to quantify the amount of regularity in data, which has potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility.
Abstract: A new statistic has been developed to quantify the amount of regularity in data. This statistic, ApEn (approximate entropy), appears to have potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility. The focus of this article is ApEn. We commence with a simple example of what we are trying to discern. We then discuss exact regularity statistics and practical difficulties of using them in data analysis. The mathematic formula development for ApEn concludes the Solution section. We next discuss the two key input requirements, followed by an account of a pilot study successfully applying ApEn to neonatal heart rate analysis. We conclude with the important topic of ApEn as a relative (not absolute) measure, potential applications, and some caveats about appropriate usage of ApEn. Appendix A provides example ApEn and entropy computations to develop intuition about these measures. Appendix B contains a Fortran program for computing ApEn. This article can be read from at least three viewpoints. The practitioner who wishes to use a "black box" to measure regularity should concentrate on the exact formula, choices for the two input variables, potential applications, and caveats about appropriate usage. The physician who wishes to apply ApEn to heart rate analysis should particularly note the pilot study discussion. The more mathematically inclined reader will benefit from discussions of the relative (comparative) property of ApEn and from Appendix A.

508 citations

Journal ArticleDOI
TL;DR: An overview of the wide varieties of techniques based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black- box’ will give a detailed understanding about seizure detection and classification, and research directions in the future.
Abstract: Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

195 citations

01 Jan 2004
TL;DR: In this article, a particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed to search the cluster center in the arbitrary data set automatically, which can help the user to distinguish the structure of data and simplify the complexity of data from mass information.
Abstract: Clustering analysis is applied generally to Pattern Recognition, Color Quantization and Image Classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model[1, 2, 3J. This method is quite simple and valid and it can avoid the minimum local value. Finally, the effectiveness of the PSO-clustering is demonstrated on four artificial data sets.

195 citations

Journal ArticleDOI
TL;DR: A technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoen coder along with the image decomposition property of wavelet transform is proposed and it is noted that the proposed method outshines the existing methods.
Abstract: Technology and the rapid growth in the area of brain imaging technologies have forever made for a pivotal role in analyzing and focusing the new views of brain anatomy and functions. The mechanism of image processing has widespread usage in the area of medical science for improving the early detection and treatment phases. Deep neural networks (DNN), till date, have demonstrated wonderful performance in classification and segmentation task. Carrying this idea into consideration, in this paper, a technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoencoder along with the image decomposition property of wavelet transform is proposed. The combination of both has a tremendous effect on sinking the size of the feature set for enduring further classification task by using DNN. A brain image dataset was taken and the proposed DWA-DNN image classifier was considered. The performance criterion for the DWA-DNN classifier was compared with other existing classifiers such as autoencoder-DNN or DNN, and it was noted that the proposed method outshines the existing methods.

183 citations