IMAGE DENOISING PROCESSING BASED ON LOCAL CONTEXTUAL HIDDEN MARKOV MODEL OF THE WAVELET TRANSFORM

2004 ◽  
Author(s):  
YILIANG WEI
Keyword(s):  
Wavelet Transform ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Image Denoising ◽  
Hidden Markov

A Discrete-Wavelet-Transform- and Hidden-Markov-Model-Based Approach for Epileptic Focus Localization

2018 ◽  
pp. 34-45 ◽  
Author(s):  
Deba Prasad Dash ◽  
Maheshkumar .H Kolekar
Keyword(s):  
Wavelet Transform ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Discrete Wavelet Transform ◽  
Hidden Markov ◽  
Focal Seizure ◽  
Epileptic Focus ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Focus Localization

Epilepsy is the most common neurological disorder with 40-50 million people suffering with it worldwide. Epilepsy is not life threatening but it disables the person to a greater extent due to its uncertainty of occurrences. Epilepsy is detected by repeated occurrences of seizure. Seizure can be generated in brain due to abnormal activity of group of neurons caused by brain tumor, genetic problem, infection, hemorrhage etc. Seizure can be detected by observing the variation in Electroencephalogram (EEG) signal. Focal seizure is defined as seizure localized in one lobe of brain. In this chapter discrete wavelet transform and Hidden Markov Model based focal seizure detection method is proposed for epileptic focus localization. EEG signal was decomposed up to level 5 using dual tree complex wavelet transform and entropy features such as collision entropy, minimum and modified sample entropy were extracted. Hidden Markov model was used for classification purpose. Maximum 80% accuracy was achieved in detecting focal and non-focal EEG signal.

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