Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

Abstract

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Sleep stage classification from heart-rate variability using long short-term memory neural networks

“…A large part of the feature set from these IBI sequences has been described in earlier work where a set of cardiac and respiratory features were evaluated 7 , however only the cardiac subset of the features is used in this work as no respiratory signal was included. The features were computed for each 30 second epoch of sleep by using a 4.5 minute window of heart beat data centred around the epoch (except when stated otherwise in Table 3).…”

Video-Based Actigraphy for Monitoring Wake and Sleep in Healthy Infants: A Laboratory Study

“…A Bayesian-based linear discriminant classification model (classifier) was deployed, which has been successfully used in sleep classification in previous studies [21,32,33]. The classifier is based on Bayes decision rules for minimizing the probability of error, i.e., to choose the class that maximizes its posterior probability given an observation (feature vector).…”

Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability

“…The probabilistic properties of sleep stage sequences and transitions are used to improve the performance of sleep stage detection using cardiorespiratory features [12]. The classifier, based on conditional random fields, achieved an average accuracy of 87.38%.…”

“…At present, many studies focus on analyzing the HRV features, and some of them are focusing on one stage distribution and transition [12]. Besides, deep learning is also used to classify sleep stage scoring.…”