An ensemble system for automatic sleep stage classification using single channel EEG signal

By Bijoy Laxmi Koley, Debangshu Dey. Comput. Biol. Medicine, 2012

299 citations recorded by the source corpus.

How researchers see this paper

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

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, methodology): “Using these features, the classiﬁcation goal is often accomplished by conventional machine learning algorithms, such as Support Vector Machine (SVM) [6], [13], k -nearest neighbors ( k -NN) [7], Random Forests [14]–[16].”
LSTM Fully Convolutional Networks for Time Series Classification (2017, methodology): “Several approaches, including feature-based [7], ensembles [8]–[10], and deep learning [11], [12], have been utilized to classify time series.”
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation (2016, methodology): “The characteristics of such features are obtained directly from the EEG epoch X ( n ) to measure certain tasks, such as central tendency, degree of dispersion, asymmetry, data peaks, troughs and ﬂatness respectively [45].”