Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit
نویسندگان
چکیده
منابع مشابه
Kernel Dimensionality Reduction on Sleep Stage Classification using ECG Signal
The purpose of this study is to apply Kernel Dimensionality Reduction (KDR) to classify sleep stage from electrocardiogram (ECG) signal. KDR is supervised dimensionality reduction method that retains statistical relationship between input variables and target class. KDR was chosen to reduce dimensionality of features extracted from ECG signal because this method doesn’t need special assumptions...
متن کاملGated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech
Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech...
متن کاملSleep apnea classification using ECG-signal wavelet-PCA features.
Sleep apnea is often diagnosed using an overnight sleep test called a polysomnography (PSG). Unfortunately, though it is the gold standard of sleep disorder diagnosis, a PSG is time consuming, inconvenient, and expensive. Many researchers have tried to ameliorate this problem by developing other reliable methods, such as using electrocardiography (ECG) as an observed signal source. Respiratory ...
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 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 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: INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS
سال: 2020
ISSN: 1598-2645,2093-744X
DOI: 10.5391/ijfis.2020.20.3.181