Publications by authors named "N J Sairamya"

Article Synopsis
  • Epilepsy is a neurological disorder characterized by frequent seizures, detected using electroencephalography (EEG) to analyze electrical activity in the brain.
  • The article reviews a dataset from 24 pediatric patients at CHB and MIT, discussing personalized medicine approaches in computer-aided diagnosis for detecting seizures from EEG signals.
  • It highlights different features extracted from EEG data, performance metrics for classification efficacy, and addresses challenges faced in automatic seizure detection using the CHB-MIT database.
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The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh-Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN).

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Electroencephalographic (EEG) signal records the neuronal activity in the brain and it is used in the diagnosis of epileptic seizure activities. Human inspection of non-stationary EEG signal for diagnosing epilepsy is cumbersome, time-consuming and inaccurate. In this paper an effective automatic approach to detect epilepsy using two feature extraction techniques namely local neighbor gradient pattern (LNGP) and symmetrically weighted local neighbor gradient pattern (SWLNGP) are proposed.

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