Fast Convolutional Method for Automatic Sleep Stage Classification
نویسندگان
چکیده
منابع مشابه
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...
متن کاملAutomatic 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 ...
متن کاملA two-step automatic sleep stage classification method with dubious range detection
BACKGROUND The limitations of the current systems of automatic sleep stage classification (ASSC) are essentially related to the similarities between epochs from different sleep stages and the subjects' variability. Several studies have already identified the situations with the highest likelihood of misclassification in sleep scoring. Here, we took advantage of such information to develop an AS...
متن کاملArtifacts processing for sleep stage classification
When a patient falls asleep, he transits by different sleep stages, which characterize the quality of his night. To determine these sleep stages, the technicians visually analyzes the polysomnographic signals (PSG) witch have different aspects according to these stages. However, this task requires a lot of time. This is why one generally tries to class them automatically. Then, one is generally...
متن کاملAutomatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold crossvalidation. We used class-balanced random sampling within the stochastic...
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: Healthcare Informatics Research
سال: 2018
ISSN: 2093-3681,2093-369X
DOI: 10.4258/hir.2018.24.3.170