Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

Migliorini, Matteo; Bianchi, Anna Maria; Nistico, Domenico; Kortelainen, Juha; Arce-Santana, Edgar R.; Cerutti, S.; Méndez, Martín O.

doi:10.1109/iembs.2010.5627217

Cited by 37 publications

(32 citation statements)

References 6 publications

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“…The sleep staging was performed using Linear Discriminant on the features extracted from the RR series and body movements (Migliorini et al, 2010). …”

Section: Methodsmentioning

confidence: 99%

Study of Heart Rate Variability in bipolar disorder: linear and nonlinear parameters during sleep

Migliorini¹

2012

Front. Neuroeng.

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The aim of the study is to define physiological parameters and vital signs that may be related to the mood and mental status in patients affected by bipolar disorder. In particular we explored the autonomic nervous system through the analysis of the heart rate variability. Many different parameters, in the time and in the frequency domain, linear and non-linear were evaluated during the sleep in a group of normal subject and in one patient in four different conditions. The recording of the signals was performed through a wearable sensorized T-shirt. Heart rate variability (HRV) signal and movement analysis allowed also obtaining sleep staging and the estimation of REM sleep percentage over the total sleep time. A group of eight normal females constituted the control group, on which normality ranges were estimated. The pathologic subject was recorded during four different nights, at time intervals of at least 1 week, and during different phases of the disturbance. Some of the examined parameters (MEANNN, SDNN, RMSSD) confirmed reduced HRV in depression and bipolar disorder. REM sleep percentage was found to be increased. Lempel–Ziv complexity and sample entropy, on the other hand, seem to correlate with the depression level. Even if the number of examined subjects is still small, and the results need further validation, the proposed methodology and the calculated parameters seem promising tools for the monitoring of mood changes in psychiatric disorders.

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“…The sleep staging was performed using Linear Discriminant on the features extracted from the RR series and body movements (Migliorini et al, 2010). …”

Section: Methodsmentioning

confidence: 99%

Study of Heart Rate Variability in bipolar disorder: linear and nonlinear parameters during sleep

Migliorini¹

2012

Front. Neuroeng.

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“…Classification is generally done using an extensive set of features. Many different classifiers have been tested over the years, such as linear discriminants (LD) [6], [8], [9], hidden Markov models (HMM) [10], and support vector machines (SVM) [3]. For many classification tasks, the LD classifier was found to be amongst the best performing.…”

Section: Introductionmentioning

confidence: 99%

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

Fonseca

Teuling

Long

et al. 2017

IEEE J. Biomed. Health Inform.

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• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. for regular subjects) for REM, and 85.69% and 0.51 (90.42% and 0.52 for regular subjects) for wake. In comparison with the stateof-the-art, and having been tested on a much larger data set, the classifier was found to outperform most of the work reported in the literature for some of the tasks, in particular for subjects with regular sleep architecture. It achieves a comparable accuracy for N3, higher accuracy and kappa for REM, and higher accuracy and comparable kappa for NREM than the best performing classifiers described in literature.

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“…In this setting, researchers have developed sensors to incorporate BCG and respiratory measurements on the bed post [3] or mattress [4][15] [16] and have been able to accurately extract vital signs such as HR and BR. Furthermore, this information has been shown to be useful in the detection of circadian rhythms and sleep patterns [15]. However, most of the previous approaches require custom-made hardware devices that require installation and alter the sleeping environment.…”

Section: Introductionmentioning

confidence: 99%

BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

Hernández¹,

McDuff²,

Picard³

2015

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

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Abstract-Continued developments of sensor technology including hardware miniaturization and increased sensitivity have enabled the development of less intrusive methods to monitor physiological parameters during daily life. In this work, we present methods to recover cardiac and respiratory parameters using accelerometer and gyroscope sensors on the wrist. We demonstrate accurate measurements in a controlled laboratory study where participants (n = 12) held three different positions (standing up, sitting down and lying down) under relaxed and aroused conditions. In particular, we show it is possible to achieve a mean absolute error of 1.27 beats per minute (STD: 3.37) for heart rate and 0.38 breaths per minute (STD: 1.19) for breathing rate when comparing performance with FDA-cleared sensors. Furthermore, we show comparable performance with a state-of-the-art wrist-worn heart rate monitor, and when monitoring heart rate of three individuals during two consecutive nights of in-situ sleep measurements.

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Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

Cited by 37 publications

References 6 publications

Study of Heart Rate Variability in bipolar disorder: linear and nonlinear parameters during sleep

Study of Heart Rate Variability in bipolar disorder: linear and nonlinear parameters during sleep

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

BioWatch: Estimation of Heart and Breathing Rates from Wrist Motions

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