Ensemble SVM Method for Automatic Sleep Stage Classification

SVM Aggregation : SVM , SVM Ensemble , SVM Classification Tree

SVM Aggregating Intelligence: SVM, SVM Ensemble, SVM Classification Tree, and Evolving SVM Classification Tree

This article scopes a concept of SVM aggregating intelligence as 3 levels research: aggregating for a better machine learning performance, aggregating for an adaptive/dynamic intelligent system, and aggregating for multitask and life-long continuous machine learning, and reviews existing SVM aggregating methods including SVM ensemble, SVM classification tree, and evolving SVM classification tree.

Self-Advising SVM for Sleep Apnea Classification

In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to transfer more information from training phase to the test phase in compare to the traditional SVM. In this paper Sleep apnea events are classified to central, obstructive or mixed, by using just three signals, airflow, abdominal and thoracic movement, as inp...

Automatic Sleep Stage Classification Using Frequency Analysis of Eeg Signals

An automated sleep stage classification system relying only on the frequency analysis of the EEG signal is developed and analyzed in this paper. The classification system consists of the feature extraction algorithm and a neural network classifier. We investigate two different feature extraction methods: a classical FFT frequency analysis and a novel LMS based feature extraction. The same two-l...

Automatic sleep stage classification using two-channel electro-oculography.

An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in ...