This work proposed a novel method for automatic sleep stage classification based on the time, frequency, and fractional Fourier transform (FRFT) domain features extracted from a single-channel electroencephalogram (EEG). Bidirectional long short-term...
IEEE journal of biomedical and health informatics
Mar 7, 2022
In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres whe...
The fluctuation of heart rate is regulated by autonomic nervous system. In human sleep, the autonomic nervous system plays a leading role. Therefore, we can use heart-rate variability (HRV) to stage the sleep process. Based on two independent public ...
BACKGROUND: Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjun...
STUDY OBJECTIVES: Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restr...
IEEE journal of biomedical and health informatics
Jul 27, 2021
Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional s...
Computer methods and programs in biomedicine
Jul 21, 2021
BACKGROUND AND OBJECTIVES: while traditional sleep staging is achieved through the visual - expert-based - annotation of a polysomnography, it has the disadvantages of being unpractical and expensive. Alternatives have been developed over the years t...
Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose...
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accurac...
We developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitg...