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What is Intelligent EEG-Based Sleep Apnea Classifier about?

This document describes an intelligent system for classifying sleep apnea using EEG signals. EEG data is preprocessed using band pass filtering and Hilbert Huang transform. The filtered EEG is divided into five frequency bands, and features like energy, entropy, and variance are extracted from each band. These features are input into machine learning classifiers like SVM, KNN, and ANN to classify sleep apnea. SVM classification produced the most promising results with an accuracy of 85%.

Author

Yasrub Siddiqui

Language

EN