2019
DOI: 10.1007/s11042-019-7359-0
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A KSOM based neural network model for classifying the epilepsy using adjustable analytic wavelet transform

Abstract: Epilepsy is a nervous disorder occurring in the cerebral cortex location of the brain which is caused by irregular harmonization of neurons. Since the existence of this disorder is between the neurons, it is tedious to diagnose correctly. Research works of epilepsy mostly done on an Electroencephalogram (EEG) signals for analyzing the neuron activity of the brain during seizures. Analyzing the continuing EEG reports manually for a patient affected by epilepsy is time-consuming, and it needs a large storage vol… Show more

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Cited by 12 publications
(8 citation statements)
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“…In significant method of feature extraction, the entropy measurements of input signals are used to distinguish emotional states of the brain. 15 Brain science and its use in the development of bioinformatics and cognitive neuroscience have been extensively used for the entropy values of the underlying structure.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…In significant method of feature extraction, the entropy measurements of input signals are used to distinguish emotional states of the brain. 15 Brain science and its use in the development of bioinformatics and cognitive neuroscience have been extensively used for the entropy values of the underlying structure.…”
Section: Related Workmentioning
confidence: 99%
“…This way of identifying emotions recognition creates a particular form of emotional recognition learned from other people's experimental evidence that can, in reality, make it possible for human subjects to identify emotions with strong generalization. In significant method of feature extraction, the entropy measurements of input signals are used to distinguish emotional states of the brain 15 . Brain science and its use in the development of bioinformatics and cognitive neuroscience have been extensively used for the entropy values of the underlying structure.…”
Section: Related Workmentioning
confidence: 99%
“…In this paper, the selective forwarding misbehaviour is overcome [3], [11][12][13] & [21], which means the malicious node in the network, deny the forwarding packets and selectively drop the packets and lack of security in the network. This mainly affects the forwarding packet transmission efficiency.…”
Section: Related Workmentioning
confidence: 99%
“…Feature extraction of machine learning technique is done in time, frequency, or time–frequency domains [ 8 ]. Time–frequency methods such as flexible analytic wavelet transform [ 9 , 10 ], short-time Fourier transform [ 11 ], discrete wavelet transform (DWT) [ 12 ], Hilber Huang transform [ 13 ], and empirical mode decomposition [ 14 ] have been considered for diagnosing epilepsy. Automatic focal and non-focal epilepsy were detected using entropy-based features from flexible analytic wavelet transform in [ 10 ].…”
Section: Introductionmentioning
confidence: 99%