Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

By Luay Fraiwan, Khaldon Lweesy, Natheer Khasawneh, Heinrich Wenz, Hartmut Dickhaus. Comput. Methods Programs Biomed., 2012

429 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3286c95d4543a1fdf93a6f3205f3d54ff166ddab

How researchers see this paper

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

In Biomedical Engineering

1. Usha R. Acharya

In Cardiology and Cardiovascular Medicine

1. Yike Guo
2. Fatemeh Afghah
3. Sajad Mousavi
4. Usha R. Acharya
5. Miad Faezipour
6. Saeid Moslehpour
7. Wafaa S. Almuhammadi
8. Khald Ali I. Aboalayon

In Cognitive Neuroscience

1. Fatemeh Afghah
2. Sajad Mousavi
3. Usha R. Acharya
4. Yike Guo
5. Peng (Paul) Wen
6. Yan Li
7. Stefanos P. Zafeiriou
8. Paul M. Matthews

Citations in the corpus

• Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders (2015, methodology): “In [6] the data consisted of 16 subjects (aged 30–75 years) and the EEG channel used was C3-A1.”
• Multi-retinal disease classification by reduced deep learning features (2017, neutral): “, it requires highly configured computation power [16–19].”
• Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal (2014, methodology): “[13] applied a random forest classifier and wavelet features to identify the wakeful state with a 90% accuracy.”
• SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019, methodology): “Then, they apply conventional machine learning algorithms such as support vector machines (SVM) [9], random forests [10], [11] and neural networks [12] to train the model for sleep stage classification based on the extracted features.”
• Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation (2016, methodology): “WT is a well-known form of the time-frequency distribution and has been widely deployed in signal processing and analysis of many fields over the past two decades [29,30,107].”
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks (2016, result): “In that work we had achieved stateof-the-art results, compared to the existing studies in [7, 17, 3], mitigated skewed sleep scoring performance in favor of the most represented sleep stages, and addressed the problem of misclassification errors due to class imbalance in the training data while sign”
• ISRUC-Sleep: A comprehensive public dataset for sleep researchers (2016, methodology): “Numerous methods have been developed for automatic etection of arousals, apnea, and sleep stages [2–5].”