Aug 2, 2024 · We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning...

Aug 2, 2024 · We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods.

Sleep apnea is one of the most common sleep breathing disorders and can lead to other serious diseases. The automated detection of sleep apnea and hypopnea even.

This study proposed a hybrid deep learning based method to combine CNN and a GRU for automatic sleep stage scoring using multi-channel biological signals (e.g., EEG, EOG, EMG).

SleepECG contains classifiers trained on large open sleep datasets. Note that the performance metrics are averages, so a classifier might perform much better or much worse for an individual record.

This project focuses on sleep onset and wake-up detection using a wrist-worn accelerometer. A Deep-GRU (Gated Recurrent Unit) architecture with multihead attention is employed to train the data.

May 25, 2022 · As we used RNN and GRU as the deep learning mediums to study the classification, we go on to suggest the feasibility of using GRU (A special form of RNN) over traditional RNN in dealing with...

This paper presents a CNN-GRU deep learning model for classifying sleep stages. The Conventional sleep stage scoring method is a visual classification process,

Sleep disorders management is paramount for maintaining overall well-being. In response to the limitations of traditional approaches, we present an innovative s.

Jul 17, 2024 · The results obtained show the GRU’s ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively.