Automated classification of neonatal sleep states using EEG
نویسندگان
چکیده
منابع مشابه
Sleep EEG Classification Using Fuzzy Logic
The computerized detection of multi stage system of EEG signals using fuzzy logic has been developed and tested on prerecorded data of the EEG of rats .The multistage detection system consists of three major stages: Awake, SWS (Slow wave sleep), REM (Rapid eye movement) which has been recorded and can be detected by the fuzzy classification and fuzzy rule base. The proposed work approaches to i...
متن کاملComplexity Analysis of Neonatal EEG Using Multiscale Entropy: Applications in Brain Maturation and Sleep Stage Classification
Automated analysis of the electroencephalographic (EEG) data for the brain monitoring of preterm infants has gained attention in the last decades. In this study, we analyze the complexity of neonatal EEG, quantified using multiscale entropy. The aim of the current work is to investigate how EEG complexity evolves during electrocortical maturation and whether complexity features can be used to c...
متن کاملAutomated Detection and Classification of Sleep Apnea Types Using Electrocardiogram (ECG) and Electroencephalogram (EEG) Features
1.1 Sleep and sleep disorders Sleep, which is defined as a passive period in organic physiology until the mid-20th century, is accepted to be an indispensable period of life cycle with today’s technological advances. While wakefulness is associated with the active excitation of Central Nervous System (CNS), sleep has been recognized as a passive period by the elimination of excitation. However,...
متن کاملAutomated detection of neonate EEG sleep stages
The paper integrates and adapts a range of advanced computational, mathematical and statistical tools for the purpose of analysis of neonate sleep stages based on extensive electroencephalogram (EEG) recordings. The level of brain dysmaturity of a neonate is difficult to assess by direct physical or cognitive examination, but dysmaturity is known to be directly related to the structure of neona...
متن کاملAutomatic 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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Clinical Neurophysiology
سال: 2017
ISSN: 1388-2457
DOI: 10.1016/j.clinph.2017.02.025