[PDF][PDF] Classification of patient by analyzing EEG signal using DWT and least square support vector machine
M Zuhair, S Thomas - Advances in Science, Technology and …, 2017 - researchgate.net
Epilepsy is a neurological disorder which is most widespread in human beings after stroke.
Approximately 70% of epilepsy cases can be cured if diagnosed and medicated properly …
Approximately 70% of epilepsy cases can be cured if diagnosed and medicated properly …
Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals
UR Acharya, SV Sree, PCA Ang, R Yanti… - International journal of …, 2012 - World Scientific
Epilepsy, a neurological disorder, is characterized by the recurrence of seizures.
Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are …
Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are …
[PDF][PDF] Brain tumor epilepsy seizure identification using multi-wavelet transform, neural network and clinical diagnosis data
M Sharanreddy, PK Kulkarni - International Journal of Computer …, 2013 - Citeseer
In the last couple of years, the EEG signal analysis was focused on epilepsy seizure
detection. Epilepsy is a common chronic neurological disorder; they are result of transient …
detection. Epilepsy is a common chronic neurological disorder; they are result of transient …
Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and …
Traditionally, detection of epileptic seizures based on the visual inspection of neurologists is
tedious, laborious and subjective. To overcome such disadvantages, numerous seizure …
tedious, laborious and subjective. To overcome such disadvantages, numerous seizure …
A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning …
Epilepsy, a common neurological disorder, is generally detected by electroencephalogram
(EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming …
(EEG) signals. Visual inspection and interpretation of EEGs is a slow, time consuming …
Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study
A Sharmila, S Aman Raj, P Shashank… - Journal of medical …, 2018 - Taylor & Francis
In this work, we have used a time–frequency domain analysis method called discrete
wavelet transform (DWT) technique. This method stand out compared to other proposed …
wavelet transform (DWT) technique. This method stand out compared to other proposed …
A nonlinear feature based epileptic seizure detection using least square support vector machine classifier
MH Kolekar, DP Dash - TENCON 2015-2015 IEEE Region 10 …, 2015 - ieeexplore.ieee.org
Epilepsy is the most common disease of central nervous system. According to World Health
Organization about 50 million people worldwide and 80% people from developing regions …
Organization about 50 million people worldwide and 80% people from developing regions …
Epileptic seizure detection using novel Multilayer LSTM Discriminant Network and dynamic mode Koopman decomposition
NV Saichand - Biomedical Signal Processing and Control, 2021 - Elsevier
Epilepsy is a neurological disorder that causes abnormality in brain function and leads to
unusual behavior, awareness loss and other defects. An Electroencephalogram (EEG) is an …
unusual behavior, awareness loss and other defects. An Electroencephalogram (EEG) is an …
[HTML][HTML] An optimized design of seizure detection system using joint feature extraction of multichannel EEG signals
The detection of seizure onset and events using electroencephalogram (EEG) signals are
important tasks in epilepsy research. The literature available on seizure detection has …
important tasks in epilepsy research. The literature available on seizure detection has …