Automated synchrogram analysis applied to heartbeat and reconstructed respiration

Hamann, Claudia; Bartsch, Ronny P.; Schumann, Aicko Y.; Penzel, Thomas; Havlin, Shlomo; Kantelhardt, Jan W.

doi:10.1063/1.3096415

Cited by 26 publications

(18 citation statements)

References 67 publications

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“…REM sleep, characterized by irregular and high-frequency waves in the electroencephalogram is associated with a lack of synchrony between neuron firing rates, possibly due to desynchronized nervous activity [43], [44]. It has been suggested by Hamann et al [45] that cardiorespiratory coordination can be observed as long as the noise from higher brain regions affects the cardiac and respiratory oscillators is uncorrelated. However, during REM sleep, when the higher brain regions are more active, long-term correlated noise might be imposed on the two oscillators and thereby cause a reduction in the cardiorespiratory coordination [45].…”

Section: Discussionmentioning

confidence: 99%

“…It has been suggested by Hamann et al [45] that cardiorespiratory coordination can be observed as long as the noise from higher brain regions affects the cardiac and respiratory oscillators is uncorrelated. However, during REM sleep, when the higher brain regions are more active, long-term correlated noise might be imposed on the two oscillators and thereby cause a reduction in the cardiorespiratory coordination [45]. It seems probable that the decrease in the amount of phase-locking between the cardiac and respiratory signals during REM sleep is caused by the desynchronized activity of the nervous system.…”

Section: Discussionmentioning

confidence: 99%

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Cardiorespiratory Phase-Coupling Is Reduced in Patients with Obstructive Sleep Apnea

et al. 2010

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Cardiac and respiratory rhythms reveal transient phases of phase-locking which were proposed to be an important aspect of cardiorespiratory interaction. The aim of this study was to quantify cardio-respiratory phase-locking in obstructive sleep apnea (OSA). We investigated overnight polysomnography data of 248 subjects with suspected OSA. Cardiorespiratory phase-coupling was computed from the R-R intervals of body surface ECG and respiratory rate, calculated from abdominal and thoracic sensors, using Hilbert transform. A significant reduction in phase-coupling was observed in patients with severe OSA compared to patients with no or mild OSA. Cardiorespiratory phase-coupling was also associated with sleep stages and was significantly reduced during rapid-eye-movement (REM) sleep compared to slow-wave (SW) sleep. There was, however, no effect of age and BMI on phase coupling. Our study suggests that the assessment of cardiorespiratory phase coupling may be used as an ECG based screening tool for determining the severity of OSA.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Cardiorespiratory Phase-Coupling Is Reduced in Patients with Obstructive Sleep Apnea

et al. 2010

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“…Power spectral analysis of cardiac inter-beat intervals can be used to characterize the components occurring at different time scales [8-12]. The influence of respiration on the cardiac beat-to-beat interval (respiratory sinus arrhythmia) can also be readily characterized by spectral analysis [13, 14]. These features make the power spectral approach superior to the time-domain approach in studies such as heart rate variability analysis.…”

Section: Introductionmentioning

confidence: 99%

Mitigating the effect of non-stationarity in spectral analysis—An application to neonate heart rate analysis

Govindan

Massaro

Niforatos

et al. 2013

Computers in Biology and Medicine

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In order to mitigate the effect of non-stationarity in frequency domain analysis of data, we propose a modification to the power spectral estimation, a widely used technique to characterize physiological signals. Spectral analysis requires partitioning data into smaller epochs determined by the desired frequency resolution. The modified approach proposed here involves dividing the data within each epoch by the standard deviation of the data for that epoch. We applied this modified approach to cardiac beat-to-beat interval data recorded from a newborn infant undergoing hypothermia treatment for birth asphyxia. The critically ill infant had episodes of tachyarrhythmia, distributed sporadically throughout the study, which affected the stationarity of the heart rate. Over the period of continuous heart rate recording, the infant’s clinical course deteriorated progressively culminating in death. Coinciding with this clinical deterioration, the heart rate signal showed striking changes in both low-frequency and high-frequency power indicating significant impairment of the autonomic nervous system. The standard spectral approach failed to capture these phenomena because of the non-stationarity of the signal. Conversely, the modified approach proposed here captured the deteriorating physiology of the infant clearly.

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“…By mathematically analyzing heart rate throughout the night, they obtained information about breathing and sleep stages of subjects [10]. …”

Section: Introductionmentioning

confidence: 99%

Sleep stage and obstructive apneaic epoch classification using single-lead ECG

et al. 2010

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BackgroundPolysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal.MethodsFor this purpose, PSG recordings (ECG included) were obtained during the night's sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed.ResultsQDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM.ConclusionThis study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.

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Automated synchrogram analysis applied to heartbeat and reconstructed respiration

Cited by 26 publications

References 67 publications

Cardiorespiratory Phase-Coupling Is Reduced in Patients with Obstructive Sleep Apnea

Cardiorespiratory Phase-Coupling Is Reduced in Patients with Obstructive Sleep Apnea

Mitigating the effect of non-stationarity in spectral analysis—An application to neonate heart rate analysis

Sleep stage and obstructive apneaic epoch classification using single-lead ECG

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