Sleep staging from Heart Rate Variability: time-varying spectral features and Hidden Markov Models

Méndez, Martín O.; Matteucci, Matteo; Castronovo, Vincenza; Ferini‐Strambi, Luigi; Cerutti, S.; Bianchi, Anna Maria

doi:10.1504/ijbet.2010.032695

Cited by 89 publications

(79 citation statements)

References 28 publications

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“…In that case it is clear that CRF achieves a higher accuracy and kappa for the 'regular' dataset, suggesting its superiority over LD for the same task, using the same base features. Regarding NREM detection, the CRF classifier achieves a comparable kappa and accuracy as the HMM classifier of Mendez et al [5] although they use a much smaller dataset limited to a narrow age range between 40 and 50 years. Regarding REM detection, CRF achieves a comparable kappa coefficient for the 'regular' dataset, and a lower coefficient for the 'all' dataset.…”

Section: Resultsmentioning

confidence: 99%

“…Cardiorespiratorybased sleep stage classification has been increasingly studied in recent years. Many studies have reported results on the classification of wake, REM, light sleep and deep sleep stages [2]- [4], detecting REM sleep [3], [5], [6], or differentiating light and deep sleep stages with an ambulatory device [3], [7]. The results in the literature are promising, but not yet at the level required for reliable sleep staging.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Previous research has established that it is possible to assess information about sleep stages using heart rate variability. [32][33][34] Knowledge about sleep stages is essential to estimate sleep quality and diagnose different sleep-related disorders. An important outlook of the ability to measure heart rate is the possibility to evaluate sleep stages using the proposed sleep monitor and therefore improve the diagnostic abilities in sleep monitoring significantly.…”

Section: Discussionmentioning

confidence: 99%

Validation of a New System Using Tracheal Body Sound and Movement Data for Automated Apnea-Hypopnea Index Estimation

Kalkbrenner

Eichenlaub

Rüdiger

et al. 2017

Journal of Clinical Sleep Medicine

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Study Objectives: The current gold standard for assessment of obstructive sleep apnea is the in-laboratory polysomnography. This approach has high costs and inconveniences the patient, whereas alternative ambulatory systems are limited by reduced diagnostic abilities (type 4 monitors, 1 or 2 channels) or extensive setup (type 3 monitors, at least 4 channels). The current study therefore aims to validate a simplified automated type 4 monitoring system using tracheal body sound and movement data. Methods: Data from 60 subjects were recorded at the University Hospital Ulm. All subjects have been regular patients referred to the sleep center with suspicion of sleep-related breathing disorders. Four recordings were excluded because of faulty data. The study was of prospective design. Subjects underwent a full-night screening using diagnostic in-laboratory polysomnography and the new monitoring system concurrently. The apnea-hypopnea index (AHI) was scored blindly by a medical technician using in-laboratory polysomnography (AHIPSG). A unique algorithm was developed to estimate the apneahypopnea index (AHIest) using the new sleep monitor. Results: AHIest strongly correlates with AHIPSG (r 2 = .9871). A mean ± 1.96 standard deviation difference between AHIest and AHIPSG of 1.2 ± 5.14 was achieved. In terms of classifying subjects into groups of mild, moderate, and severe sleep apnea, the evaluated new sleep monitor shows a strong correlation with the results obtained by polysomnography (Cohen kappa > 0.81). These results outperform previously introduced similar approaches. Conclusions:The proposed sleep monitor accurately estimates AHI and diagnoses sleep apnea and its severity. This minimalistic approach may address the need for a simple yet reliable diagnosis of sleep apnea in an ambulatory setting. Clinical Trial Registration: Trial name: Validation of a new method for ambulant diagnosis of sleep related breathing disorders using body sound; URL: https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011195; Identifier: DRKS00011195 Keywords: monitoring, movement analysis, respiratory sounds, sleep apnea Citation: Kalkbrenner C, Eichenlaub M, Rüdiger S, Kropf-Sanchen C, Brucher R, Rottbauer W. Validation of a new system using tracheal body sound and movement data for automated apnea-hypopnea index estimation. J Clin Sleep Med. 2017;13(10):1123-1130. I NTRO DUCTI O NWith a prevalence of 4% in adult men and 2% in women, obstructive sleep apnea (OSA) is one of the most common sleeprelated breathing disorders.1 Additionally, more than 75% of people suffering from moderate OSA are either undiagnosed or untreated.2 OSA is characterized by multiple breathing cessations during the night due to different possible causes. If untreated, this disorder can lead to extensive daytime sleepiness 3 and an elevated risk for cardiovascular disease. [4][5][6] The main criteria used to indicate the severity of OSA is the apneahypopnea index (AHI), which describes the mean number of breathing pa...

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“…Virkkala et al have classified the sleep stages using only EOG signals with the agreement of 72% [16]. Mendez et al have utilized a Hidden Markov Model (HMM) with spectral features of heart rate variability to classify NREM and REM and the classification accuracy is measured around 80% in both training and test sets [17]. Liang et al have presented a rulebased sleep-stage classification method using features of temporal and spectral analyses of the EEG, EOG, and EMG signals with an agreement rate of 86.68% [18].…”

Section: Introductionmentioning

confidence: 99%

Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals

İlhan

Bilgin

2017

ijisae

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Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for more robust and efficient diagnosis systems. Several traditional machine-learning and pattern recognition algorithms are deployed on the modern computer aided diagnosis systems. However, current results are not as satisfactory as expected. In the last two decade, a new concept has emerged with 'ensemble learning' title. It has attracted the attention of many researchers from various disciplines. In this study, several ensemble-learning methods are utilized and inspected on EEG signals for sleep-stage classification. Conventional machine-learning methods are also performed in same testing phase to report comparative results. Additionally, methods are evaluated in two different scenarios; subject specific and independent. Study proves that combination of DTs and SVMs in bagging theorem surpasses all of the conventional methods used in the experiments. Moreover, test trials reveal that both conventional and ensemble models need to be improved for subject independent scenario which is more essential case in the development of patient independent computer based diagnosis systems.

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Sleep staging from Heart Rate Variability: time-varying spectral features and Hidden Markov Models

Cited by 89 publications

References 28 publications

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

Cardiorespiratory Sleep Stage Detection Using Conditional Random Fields

Validation of a New System Using Tracheal Body Sound and Movement Data for Automated Apnea-Hypopnea Index Estimation

Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals

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