Sleep staging using cardiorespiratory signals

Redmond, Stephen J.; Chazal, Philip de; O’Brien, Ciara; Ryan, Suzanne; McNicholas, Walter T.; Heneghan, Conor

doi:10.1007/s11818-007-0314-8

Cited by 84 publications

(100 citation statements)

References 29 publications

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“…The generative approach for heartbeat detection selects those heartbeat positions that minimize the residual error . The algorithm has been validated with a 40-patient group at Helsinki Sleep Clinic, Helsinki, Finland 1 . Preliminary results from the validation show over 98% precision with all the patients even though some of them had very challenging signals due to pathologies.…”

Section: B Signal Analysismentioning

confidence: 99%

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Unobtrusive online monitoring of sleep at home

Paalasmaa

Waris

Toivonen

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-We describe an online sleep monitoring service, based on unobtrusive ballistocardiography (BCG) measurement in an ordinary bed. The novelty of the system is that the sleep tracking web application is based on measurements from a fully unobtrusive sensor. The BCG signal is measured with a piezoelectric film sensor under the mattress topper, and sent to the web server for analysis. Heart rate and respiratory variation, activity, sleep stages, and stress reactions are inferred based on the signal. The sleep information is presented to the user along with measurements of the sleeping environment (temperature, noise, luminosity) and user-logged tags (e.g. stress, alcohol, exercise). The approach is designed for longterm use at home, allowing users to follow the development of their sleep over months and years. The service has also a medical use, as sleep disorder patients can be measured for long periods before and after interventions.

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Section: B Signal Analysismentioning

confidence: 99%

“…Inferring sleep stages based on the measurement of heart rate variability, respiration rate variability and/or activity has been proposed by many research groups [1]- [5]. These approaches are based on extracting various features from these parameters and using a classifier to map them to sleep stages.…”

Section: Introductionmentioning

confidence: 99%

Unobtrusive online monitoring of sleep at home

Paalasmaa

Waris

Toivonen

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…where µ µ µ a a a and Σ Σ Σ are the mean vector for class ω a and the pooled covariance matrix (Duda et al, 2001;Redmond et al, 2007). To use this function in the training step of our classification framework, we need to compute the sample mean and the prior probabilities of each class and the inverse pooled covariance matrix Σ Σ Σ.…”

Section: Classification Frameworkmentioning

confidence: 99%

“…Regarding the prior probabilities P (ω a ) of each class, we used the observation that the different classes have different probabilities throughout the night (Redmond et al, 2007). The time-dependent prior probabilities for a given class can be obtained by counting, for each epoch relative to the beginning (i.e., when lights were turned off) of each recording, the number of times that epoch was annotated with that class.…”

Section: Classification Frameworkmentioning

confidence: 99%

“…By evaluating how these parameters change, it should be possible, at least to a certain extent, to distinguish some of these stages without resorting to EEG. Several research groups have worked on the extraction of cardiorespiratory and body movement features (e.g., Devot et al (2007), Devot et al (2010), Redmond et al (2007) or Zoubek et al (2007)). However, one of the main issues is that many publications address the sleep stage classification problem from a rather limited set of physiological features.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Feature Selection for Sleep/Wake Classification with Small Data Sets

Foussier¹,

Fonseca²,

Long

et al. 2013

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining

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Sleep staging using cardiorespiratory signals

Cited by 84 publications

References 29 publications

Unobtrusive online monitoring of sleep at home

Unobtrusive online monitoring of sleep at home

Automatic Feature Selection for Sleep/Wake Classification with Small Data Sets

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

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