Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach

The crosstalk between organs plays a crucial role in physiological processes. This coupling is dynamical process, it must cope with huge variety of rhythms frequencies ranging from milliseconds to hours, days, seasons. brain central hub for this crosstalk. During sleep, automatic rhythmic interrelations are enhanced and provide direct insight into organ dysfunctions, however their origin remains difficult issue, particular sleep disorders. In study, we focus on EEG, ECG, airflow recordings polysomnography databases. Because these signals non-stationary, non-linear, noisy, span wide spectral ranges, time-frequency analysis, based wavelet transforms, more appropriate handle complexity. We design wavelet-based extraction method identify the characteristic different signals, temporal variability. These new constructs combined pairs compute complex coherence. coherence maps highlight occurrence slowly modulated pattern frequency range [0.01–0.06] Hz, which appears both obstructive apnea. A preliminary exploration large database National Sleep Research Resource respiration disorders, such as apnea provides some clues its relation autonomic cardio-respiratory rhythms. also observe that during episodes (either or central), cardiopulmonary (in respiratory sinus-arrhythmia) [0.1–0.7] Hz strongly diminishes, suggesting modification coupling. Finally, comparing time-averaged heart rate variability spectra episodes, discuss common trait differences.