[PDF] Top 20 EPILEPSY DETECTION USING STATISTICAL FEATURES ON EEGSIGNAL
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EPILEPSY DETECTION USING STATISTICAL FEATURES ON EEGSIGNAL
... the epilepsy, the normal pattern of neurons activity becomes disturbed causing strange sensation, emotion, behavior and loss of consciousness (Engel, 1989; Robert, ...2005). Epilepsy is a disorder due to ... See full document
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Classification of Normal and Epileptic EEG Signals Using Simple Statistical Feature Extraction
... seizure detection from recorded EEG signals for a healthy and epileptic ...decomposition using discrete wavelet transform (DWT) After DWT decomposition, a statistical feature for epilepsy ... See full document
7
Weighted Visibility Graph with Complex Network Features in the Detection of Epilepsy
... for detection of epileptic syndrome because the complex network with seizure activity exhibit different edge weight value which further affect their statistical attributes ...two statistical ... See full document
13
An Automated System for Indian Currency Denomination Identification
... surface features of exchange note ...coefficient statistical instants are removed from the approximate Coefficient ...extracted features are formed into feature vector which may be employed for ... See full document
9
Headache in epilepsy: prevalence and clinical features
... the Epilepsy and Headache ...and features of ...on epilepsy syndrome, seizure types, frequency of sei- zures, epilepsy etiology, age at epilepsy onset, disease duration, current ... See full document
10
Error Detection Using Linguistic Features
... Linguistic features from syntactic, semantic, and dialogue dis- course analyses have proven their values in error de- tection in domain specific spoken dialogue systems, ...error detection in dictation, a ... See full document
8
Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy
... exudates detection using mathematical morphology approach (Section ...their features such as status of exudates(s), minimal and maximum values corresponding to the aspect ratios, eccentricity, area ... See full document
10
Statistical Features and Traditional SA SVM Classification Algorithm for Crack Detection
... DOI: 10.4236/jsip.2018.92007 113 Journal of Signal and Information Processing ture set to feed the machine learning approaches, while in this paper the combi- nation of the three damage indexes which are obtained between ... See full document
11
Epilepsy Detection by Processing of EEG Signals using Conventional Method
... the epilepsy occurs is due to the abnormal sparking of the neurons which inturn is caused by the irregular activities within the ...of epilepsy gives some instincts or appearance symbols some few hours ... See full document
12
NEUROLOGICAL DISORDERS: AN OVERVIEW
... Epilepsy: Epilepsy is the most common of chronic neurological disorders and it imposes the biggest burden on health care ...clinical features, aetiology, severity, and prognosis and its association ... See full document
11
Automatic detection of the HFO zone in epilepsy using magnetoencephalography
... To avoid missing HFOs in the brain, the whole brain volume has to be sampled by VSs. The locations where the beamformer algorithm calculates VSs were based on a list of x- y- z-coordinates. These coordinates were ... See full document
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Automatic epilepsy detection using fractal dimensions segmentation and GP–SVM classification
... new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and clas- ... See full document
11
Epilepsy Detection by Processing of EEG Signals using Labview Simulation
... read using the Lab VIEW biomedical ...out using the specific filter that is being designed and thus the specific EEG rhythm is ...extracted using single tone measurement ... See full document
11
Style classification and visualization of art painting’s genre using self-organizing maps
... various features from paintings is suggested. Global features are extracted using the color‑based statistical computation and composition‑ based local features of paintings are ... See full document
11
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... the statistical analysis, static and dynamic analysis had been applied to identify the vulnerability being exploited by the worm in the binary code, to identify flows of the code, damage implication, the expected ... See full document
6
Breast tumor detection and classification in Mammograms: Gabor wavelet vs. statistical features
... Breast cancer is the second cause of fatality among all cancers for women. Automatic classification of breast cancer lesions in mammograms is a challenging task due to the irregularity and complexity of the location, ... See full document
12
A mutual information based adaptive windowing of informative EEG for emotion recognition
... To overcome these problems, this paper introduces a subject-dependent mutual information-based windowing method for extracting informative EEG features for robust and accurate classification of the associated ... See full document
17
A Survey on Lfun Approach Using Statistical Features-Based Real-Time Twitter Spam Detection
... spam detection, which make utilization of the measurable highlights of ...the statistical properties of spam tweets differ after some time, and the performance of existing machine learning-based classifiers ... See full document
7
Epilepsy with auditory features
... 2). Epilepsy or seizures are re- ported in many of the patients with this deletion, but they are not commonly the primary diagnosis and are therefore not described in ...Accurate epilepsy phenotyping in ... See full document
10
A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Character Co occurrence
... A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Character Co occurrence A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Ch[.] ... See full document
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