Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks.

By Ling Guo, Daniel Rivero, Julian Dorado, Juan Ramón Rabuñal, Alejandro Pazos. Journal of Neuroscience Methods, 2010

447 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a2c94e282937b3392c8f0b99b74e123650b3d834

How researchers see this paper

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

In Cognitive Neuroscience

1. Usha R. Acharya
2. Ram Bilas Pachori
3. Subbhuraam Vinitha Sree
4. Apiwat Lek-uthai
5. Poomipat Boonyakitanont
6. Jitkomut Songsiri
7. Krisnachai Chomtho
8. Jasjit S. Suri

In Signal Processing

1. Usha R. Acharya
2. Ram Bilas Pachori
3. Apiwat Lek-uthai
4. Poomipat Boonyakitanont
5. Jitkomut Songsiri
6. Krisnachai Chomtho
7. G. Swapna
8. Muhammad Hussain

Citations in the corpus

• An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach (2018, background): “In another study, Guo et al. (Guo et al., 2010) achieved the accuracy of 97.77% for ABCD-E case on the same dataset.”
• Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition (2012, methodology): “The method based on line length feature of the sub-band signals presented in [15] has provided almost similar classification accuracy that is obtained in [8] with lower computational complexity.”
• Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, unknown): “[43] 2010 Seizure detection Lima et al.”
• Automated EEG analysis of epilepsy: A review (2013, methodology): “60% using wavelet transform and line length feature [42], and an accuracy of 99% using genetic programming based features in a K-Nearest Neighbor (KNN) classifier [43].”
• A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “While some studies have considered a particular group of features applicable to their proposed classification method [Got82, GRD + 10, OLC + 09], others have applied various groups of features extracted from the time, frequency, and time-frequency domains.”
• Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals (2022, background): “In addition, the low-frequency Figure 2 is the comparison between the original signal and VMF1, VMF2, and VMF3 when K = 5 in the Bonn dataset. portion of the EEG signal often contains more useful information for seizure detection [42].”