Corpus ID: 86635876

Brain Data Mining for Epileptic Seizure-Detection

@inproceedings{Siddiqui2018BrainDM,
  title={Brain Data Mining for Epileptic Seizure-Detection},
  author={Mohammad Khubeb Siddiqui},
  year={2018}
}
Humans are suffering from various neurological disorders, due to the sedentary life style with stress and anxiety. Epilepsy is one of them, which is the second most prevalent neurological disorder after brain stroke. It has affected 65 million people, which are approximately 1% of global population, with 80% of the patients being from developing countries. At every moment, a human brain generates signals, which communicate with each other. Once this communication breaks down or any abnormal… Expand
1 Citations
A review of epileptic seizure detection using machine learning classifiers
TLDR
An overview of the wide varieties of techniques based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black- box’ will give a detailed understanding about seizure detection and classification, and research directions in the future. Expand

References

SHOWING 1-10 OF 180 REFERENCES
Automated epileptic seizure onset detection
  • A. Dorai, K. Ponnambalam
  • Computer Science
  • 2010 International Conference on Autonomous and Intelligent Systems, AIS 2010
  • 2010
TLDR
New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictAL, and postictal states in the brain. Expand
A machine learning system for automated whole-brain seizure detection
TLDR
A supervised machine learning approach is presented that classifies seizure and non-seizure records using an open dataset containing 342 records and posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Expand
Unsupervised EEG analysis for automated epileptic seizure detection
TLDR
This work presents an unsupervised technique to discriminate seizures and non-seizures events without having any prior knowledge on patient's history and employs power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non -seizure events. Expand
Analysis of EEG Signals for Detection of Epileptic Seizure Using Hybrid Feature Set
TLDR
An automated system which will detect epileptic seizure without involving an expert opinion is provided and provides an average accuracy of 86.93 %. Expand
A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine
TLDR
Results show that the proposed automatic epilepsy detection system which uses sample entropy (SampEn) as the only input feature, together with extreme learning machine (ELM) classification model, not only achieves high classification accuracy (95.67%) but also very fast speed. Expand
Complexity Measures for Normal and Epileptic EEG Signals using ApEn, SampEn and SEN
There are numerous applications of EEG signal processing such as monitoring alertness, coma, and brain death, controlling an aesthesia, investigating epilepsy and locating seizure origin, testingExpand
Automated Epileptic Seizure Detection Methods: A Review Study
TLDR
The hallmark of epilepsy is recurrent seizures termed "epileptic seizures", a neurological condition in which an individual experiences chronic abnormal bursts of electrical discharges in the brain. Expand
Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks
TLDR
A novel method for automatic epileptic seizure detection is presented, which uses line length features based on wavelet transform multiresolution decomposition and combines with an artificial neural network (ANN) to classify the EEG signals regarding the existence of seizure or not. Expand
A novel quick seizure detection and localization through brain data mining on ECoG dataset
TLDR
The initial experiments indicate that decision forest algorithms such as SysFor and Forest CERN can reduce the seizure detection time significantly while maintaining 100% accuracy, and can also be used to identify the region of the brain of a patient that is mostly affected by seizure. Expand
Data mining approach in seizure detection
TLDR
The seizure states are classified and motivates the importance of electrodes which are placed on the brain surface and suggests through the systematic forest (SysFor) a type of decision forest that some electrodes are more fruitful to detect the seizure and found two features ‘min’ and ‘max’ are the best features. Expand
...
1
2
3
4
5
...