Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction

By E. Alickovic, Jasmin Kevric, Abdulhamit Subasi. Biomedical Signal Processing and Control, 2018

367 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/956faaabd611145c6e06f4f6d68e41eda28e0202

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. Rubén Morales-Menéndez
2. Abbas Khosravi
3. Xiaodi Huang
4. Saeid Nahavandi
5. Yanfang Sang
6. Mohammad Khubeb Siddiqui
7. Nasir Hussain

In Signal Processing

1. Xiaodi Huang
2. Amir F. Atiya
3. Yanfang Sang

Citations in the corpus

• Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, background): “Many machine learning algorithms have been developed using statistical, frequency domain and nonlinear parameters to detect epileptic seizures [10], [11], [12], [13], [14], [15].”
• Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “[64] have recently proved that WPD gives a better performance than DWT.”
• A Review on Machine Learning for EEG Signal Processing in Bioengineering (2020, background): “played a pivotal role in machine learning for EEG such as the following studies [6].”
• A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “Subasi et al. [96] achieved 100% of accuracy using four machine learning classifiers such as ANN, KNN, SVM, and random forest on two popular datasets—Freiburg and CHB-MIT to classify the three different states of seizures ‘pre-ictal’, ‘ictal’, and ‘inter-ictal’.”