Entropies for detection of epilepsy in EEG

By N. Kannathal, Min Choo, Usha R. Acharya, P. Sadasivan. Comput. Methods Programs Biomed., 2005

800 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bb699338b1f5818a9af15b828fbad21feacd86c4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Steven L. Brunton
2. J. Nathan Kutz
3. Joshua L. Proctor
4. Bingni W. Brunton
5. Eurika Kaiser
6. Subbhuraam Vinitha Sree
7. Usha R. Acharya
8. Jasjit S. Suri

In Cognitive Neuroscience

1. Usha R. Acharya
2. Steven L. Brunton
3. Subbhuraam Vinitha Sree
4. Jasjit S. Suri
5. Roshan Joy Martis
6. J. Nathan Kutz
7. G. Swapna
8. Joshua L. Proctor

In Molecular Biology

1. J. Nathan Kutz
2. Steven L. Brunton
3. Eurika Kaiser
4. Joshua L. Proctor
5. Bingni W. Brunton
6. Dimitrios I. Fotiadis
7. Alexandros T. Tzallas
8. Markos G. Tsipouras

Citations in the corpus

• Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy (2009, result): “The study demonstrated that entropy values computed for the epileptic EEG were lower compared to the values computed for the normal EEG (Kannathal et al., 2005).”
• EEG Signal Analysis: A Survey (2010, methodology): “Different entropies have been used to analyze normal and epileptic EEG signals [41].”
• Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis (2009, methodology): “Principal component analysis (PCA), using 1% threshold, was employed to reduce the dimension of the feature set [44] resulting in three to four features in all cases.”
• Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals (2011, methodology): “Various entropies were used to detect normal and epileptic EEG signals using Adaptive Neuro Fuzzy Inference System (ANFIS).(34) A detection accuracy of 92.”
• Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. (2010, methodology): “In the research of Kannathal et al. (2005), several entropy measures were investigated for discriminating EEG signals.”
• Chaos as an intermittently forced linear system (2016, background): “A time series of voltage is recorded from these electrodes, and although typically the spectral content of EEG is analyzed, there are also numerous examples of time series EEG analysis [77, 3, 47, 92, 60].”
• Automated EEG analysis of epilepsy: A review (2013, methodology): “The random phase spectrum of the derived surrogate data can be achieved by any of the three methods of random phase, phase shuffle and data shuffle [59].”
• Microstates in Resting-State EEG: Current Status and Future Directions (2014, methodology): “Several methods to define the entropy as a state characteristic of the EEG signal have been proposed to recognize ictal patterns (Kannathal et al., 2005).”
• Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks (2007, methodology): “Chaotic features [28–31] such as correlation dimension [32, 33], Lyapunov exponents [34], and entropy [35] have also been proposed to characterize the EEG signal.”
• A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “Whilst some researchers used many features [34, 79], the others applied a few features [31, 36, 108, 112, 137] for detecting the seizure.”