Signal processing techniques applied to human sleep EEG signals—A review
A bewildering variety of methods for analysing sleep EEG signals can be found in the
literature. This article provides an overview of these methods and offers guidelines for …
literature. This article provides an overview of these methods and offers guidelines for …
A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
Biomedical signals carry signature rhythms of complex physiological processes that control
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …
our daily bodily activity. The properties of these rhythms indicate the nature of interaction …
Learning machines and slee** brains: automatic sleep stage classification using decision-tree multi-class support vector machines
T Lajnef, S Chaibi, P Ruby, PE Aguera… - Journal of neuroscience …, 2015 - Elsevier
Background Sleep staging is a critical step in a range of electrophysiological signal
processing pipelines used in clinical routine as well as in sleep research. Although the …
processing pipelines used in clinical routine as well as in sleep research. Although the …
An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24× 7 utilizing the Siesta database
P Anderer, G Gruber, S Parapatics, M Woertz… - …, 2005 - karger.com
To date, the only standard for the classification of sleep-EEG recordings that has found
worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even …
worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even …
An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model
Objective Sleep stage scoring is essential for diagnosing sleep disorders. Visual scoring of
sleep stages is very time-consuming and prone to human errors. In this work, we introduce …
sleep stages is very time-consuming and prone to human errors. In this work, we introduce …
Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series
I Visser - Journal of Mathematical Psychology, 2011 - Elsevier
This paper provides a tutorial on key issues in hidden Markov modeling. Hidden Markov
models have become very popular models for time series and longitudinal data in recent …
models have become very popular models for time series and longitudinal data in recent …
Promises and challenges in the use of consumer-grade devices for sleep monitoring
The market for smartphones, smartwatches, and wearable devices is booming. In recent
years, individuals and researchers have used these devices as additional tools to monitor …
years, individuals and researchers have used these devices as additional tools to monitor …
A reliable probabilistic sleep stager based on a single EEG signal
Objective: We developed a probabilistic continuous sleep stager based on Hidden Markov
models using only a single EEG signal. It offers the advantage of being objective by not …
models using only a single EEG signal. It offers the advantage of being objective by not …
A transition-constrained discrete hidden Markov model for automatic sleep staging
Background Approximately one-third of the human lifespan is spent slee**. To diagnose
sleep problems, all-night polysomnographic (PSG) recordings including …
sleep problems, all-night polysomnographic (PSG) recordings including …
Sleep staging from heart rate variability: time-varying spectral features and hidden Markov models
An alternative DSS which models the behaviour of the Heart Rate Variability (HRV) signal
linked to stable (NREM) and instable (REM) cerebral waves during sleep and a probabilistic …
linked to stable (NREM) and instable (REM) cerebral waves during sleep and a probabilistic …