An Electrocardiogram-based Technique to Assess Cardiopulmonary Coupling During Sleep

Study Objectives: To evaluate a new automated measure of cardiopulmonary coupling during sleep using a single-lead electrocardiographic signal.

Design: Using training and test datasets of 35 polysomnograms each, we assessed the correlations of an electrocardiogram-based measure of cardiopulmonary interactions with respect to standard sleep staging, as well as to the cyclic alternating pattern classification. The pattern of coupling in 15 healthy individuals was also assessed.

Setting: American Academy of Sleep Medicine Accredited Sleep Disorders Center.

Interventions: None.

Measurements And Results: From a continuous, single-lead electrocardiogram, we extracted both the normal-to-normal sinus interbeat interval series and a corresponding electrocardiogram-derived respiration signal. Employing Fourier-based techniques, the product of the coherence and cross-power of these 2 simultaneous signals was used to generate a spectrographic representation of cardiopulmonary coupling dynamics during sleep. This technique shows that non-rapid eye movement sleep in adults demonstrates spontaneous abrupt transitions between high- and low-frequency cardiopulmonary coupling regimes, which have characteristic electroencephalogram, respiratory, and heart-rate variability signatures in both health and disease. Using the kappa statistic, agreement with standard sleep staging was poor (training set 62.7%, test set 43.9%) but higher with cyclic alternating pattern scoring (training set 74%, test set 77.3%).

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 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 sleep stages.