An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea.

Authors:
Harrington J, Schramm PJ, Davies CR, Lee-Chiong TL Jr.

Reference:
Sleep Breath. 2013 Sep;17(3):1071-8.

Objectives:
The study compares polysomnography (PSG) and cardiopulmonary coupling (CPC) sleep quality variables in patients with (1) obstructive sleep apnea (OSA) and (2) successful and unsuccessful continuous positive airway pressure (CPAP) response.

Conclusions:
Tests differentiated no and moderate to severe OSA groups by REM %, HFC, VLFC, and LFC/HFC ratio variables. The successful CPAP therapy group had more HFC, less LFC, and e-LFCBB compared to the unsuccessful CPAP therapy group. HFC ≥ 50 % showed high sensitivity (77.8 %) and specificity (88.9 %) in identifying successful CPAP therapy.

Practical Significance:
The results support the use of the SleepImage system to investigate and objectively measure sleep quality in patients complaining of a sleep disorder.

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An Electrocardiogram-based Technique to Assess Cardiopulmonary Coupling During Sleep Authors

Authors:
Thomas RJ, Mietus JE, Peng CK, Goldberger AL

Reference:
SLEEP 2005;28:1151-1161

Objectives:
Evaluate a new automated measure of CPC during sleep using a single-lead electrocardiographic (ECG) signal

Conclusions:
A sleep spectrogram derived from information in a single lead electrocardiogram can be used to dynamically track cardiopulmonary interactions. The 2 distinct (bimodal) regimes demonstrate a closer relationship with visual cyclic alternating pattern (CAP) and non-cyclic alternating pattern states than with standard sleep stages. This technique may provide a complementary approach to the conventional characterization of graded non-rapid eye movement (NREM) sleep stages

Practical Significance:
This seminal work establishes the link between High Frequency CPC with good sleep quality, and Low Frequency CPC with poor quality sleep

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Differentiating Obstructive from Central & Complex Sleep Apnea Using an Automated Electrocardiogram-based Method

Authors:
Thomas RJ, Mietus JE, Peng CK, Gilmartin G, Daly RW, Goldberger AL, Gottlie DJ.

Reference:
Sleep. 2007 Dec;30(12):1756-69.

Objectives:
Complex sleep apnea is defined as sleep disordered breathing secondary to simultaneous upper airway obstruction and respiratory control dysfunction. The objective of this study was to assess the utility of an electrocardiogram (ECG) based CPC technique to distinguish obstructive from central or complex sleep apnea

Conclusions:
ECG based spectral analysis allows automated, operator-independent characterization of probable interactions between impaired respiration and upper airway anatomical obstruction. The clinical utility of spectrographic classification, especially in predicting failure of positive airway pressure therapy, remains to be more thoroughly tested

Practical Significance:
Using the Heart Health Study population of 3989 subjects, this study shows that CPC not only differentiated obstructive vs. central vs. complex sleep apnea, but it positively correlated with periodic breathing episodes in PSG and was the strongest predictor of success or failure with PAP titration.

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HHT based cardiopulmonary coupling analysis for sleep apnea detection

Authors:
Dongdong Liu, Xiaochen Yang, Guangfa Wang, Jing Ma, Yanhui Liu, Chung-Kang Peng, Jue Zhang, Jing Fang

Reference:
Sleep Medicine 13(5):503-509, May 2012

Objectives:
To validate the feasibility of the Hilbert–Huang transform (HHT) based cardiopulmonary coupling (CPC) technique in respiratory events detection and estimation of the severity of apnea/hypopnea.

Conclusions:
The HHT-CPC spectrum provides much finer temporal resolution and frequency resolution (8s and 0.001Hz) compared with the original CPC (8.5min and 0.004Hz). The area under the ROC curve of pLFC was 0.79 in distinguishing respiratory events from normal breathing. Significant differences were found in TVDF among groups with different severities of OSAHS (normal, mild, moderate, and severe, p<0.001). TVDF has a strong negative correlation with the apnea/hypopnea index (AHI, correlation coefficient −0.71).

Practical Significance:
The spectrographic markers, pLFC and TVDF can be used to identify respiratory events and represent the disruption extent of sleep architecture in patients with sleep apnea/hypopnea, respectively.

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Mapping Sleep Using Coupled Biological Oscillations

Authors:
Thomas RJ, Mietus JE

Reference:
Conf. Proc IEEE Eng Med Biol. Soc. 2011;2011:1479-82.

Objectives:
To examine the utility of an electrocardiogram-derived sleep spectrogram to provide a different view of sleep

Conclusions:
Non-electroencephalogram (EEG) recordings can provide an alternative approach to viewing sleep quality.

Practical Significance:
Novel insights into physiology and pathology of sleep can be obtained through the coupling of ECG and respiratory signal influences on the ECG R wave.

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Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep.

Authors:
Thomas RJ, Mietus JE, Peng CK2, Guo D, Gozal D, Montgomery-Downs H, Gottlieb DJ, Wang CY, Goldberger AL.

Reference:
Sleep Med. 2014 Jan; 15(1):125-31.

Objectives:
To evaluate the hypothesis that that slow-wave EEG power would show a relatively fixed-time relationship to periods of high-frequency CPC.

Conclusions:
The overall correlation (r) between delta power and high-frequency coupling (HFC) power was statistically significant.

Practical Significance:
The results support a tight temporal relationship between EEG slow wave power and high frequency cardiopulmonary coupling.

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Technical advances in the characterization of the complexity of sleep and sleep disorders.

Authors:
Bianchi MT, Thomas RJ.

Reference:
Prog Neuropsychopharmacol Biol Psychiatry. 2013 Aug 1;45:277-86.

Objectives:
A review of the spectrum of approaches that have been leveraged towards improved understanding of the complexity of sleep.

Conclusions:
The complexity of sleep physiology has inspired alternative metrics that are providing additional insights into the rich dynamics of sleep. Electro-encephalography, magneto-encephalography, and functional magnetic resonance imaging represent advanced imaging modalities for understanding brain dynamics.

Practical Significance:
These methods are complemented by autonomic measurements that provide additional important insights.

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