Comparison of two different approaches in the detection of intermittent cardiorespiratory coordination during night sleep

Abstract

Background: The objective was to evaluate and to compare two completely different detection algorithms of intermittent (short-term) cardiorespiratory coordination during night sleep. The first method is based on a combination of respiratory flow and electrocardiogram recordings and determines the relative phases of R waves between successive onsets of inspiration. Intermittent phase coordination is defined as phase recurrence with accuracy alpha over at least k heartbeats. The second, recently introduced method utilizes only binary coded variations of heart rate (acceleration = 1, deceleration = 0) and identifies binary pattern classes which can be assigned to respiratory sinus arrhythmia (RSA). It is hypothesized that RSA pattern class recurrence over at least k heartbeats is strongly related with the intermittent phase coordination defined above.

Results: Both methods were applied to night time recordings of 20 healthy subjects. In subjects <45 yrs and setting k = 3 and alpha = 0.03, the phase and RSA pattern recurrence were highly correlated. Furthermore, in most subjects the pattern predominance (PP) showed a pronounced oscillation which is most likely linked with the dynamics of sleep stages. However, the analysis of bivariate variation and the use of surrogate data suggest that short-term phase coordination mainly resulted from central adjustment of heart rate and respiratory rate rather than from real phase synchronization due to physiological interaction.

Conclusion: Binary pattern analysis provides essential information on short-term phase recurrence and reflects nighttime sleep architecture, but is only weakly linked with true phase synchronization which is rare in physiological processes of man.

Figure 1

Method 1 vs. Method 2: Gray-scale maps of subject b05. Upper diagram: (a) gray-scale plot of the relative frequency f₁(i,m) of intermittent m:2 phase coordination within a 1001-heartbeat window centered around the ith heartbeat and plotted against the time of day of the ith heartbeat; (b) weighted phase coordination ratio PCR₁ according to equation (6) (black line); (c) phase recurrency PR as the mean of the two maximal f₁ values in vertical direction (gray line) Lower diagram: (a) gray-scale plot of the corresponding normalized frequency f₂(i,m) of m:2 RSA pattern recurrence within a 1001-heartbeat window centered around the ith heartbeat and plotted against the time of day of the ith heartbeat; (b) weighted phase coordination ratio PCR₂ according to equation (6) (black line); (c) pattern predominance PP as the mean of the two maximal f₂ values in vertical direction (gray line)

Figure 2

Method 1 vs. Method 2: Correlation diagrams of subject b05. PCR₁ vs. PCR₂ and PR vs. PP of the data (black and gray lines) in Fig. 1. In this subject only the weighted phase coordination PCR is highly reproducible. In other subjects also the PR-PP correlation (see linear correlation coefficient r₃ in Table 1) is striking.

Figure 3

Coordination vs. HRV in subject b05. HRV parameters LF, HF and BAL vs. PR (upper diagrams) and PP (lower diagrams). The high correlation between PP and BAL confirms the methodological link between both parameters (see discussion) and supports the discussed dependency on sleep stages which has recently been shown for BAL.

Figure 4

Night sleep oscillations of PP and BAL in subject b05. The negative correlation between PP (gray line) and BAL (black line) is also revealed by the mirrored time course of both parameters. In subject b05 BAL and PP show similar oscillatory fluctuations during sleep which probably relate to the periodic succession of sleep stages.

Figure 5

Night sleep oscillations of PP and BAL in subject b06. In subject b06 the correlation between PP (gray line) and BAL (black line) is weaker but, most interestingly, PP still oscillates while BAL does not. Is PP a better marker for sleep stage related alterations of cardiac control?

Figure 6

Shuffled surrogate data and bivariate variation (subject b05). Shuffling RR intervals within successive windows of 50 heartbeats destroyed all deterministic properties of the RR series on the LF-HF time scale while preserving long-term RR variability. The respiratory data remained unchanged. Despite this massive data manipulation the resulting gray-scale map of f₁ (lower diagram) does not change considerably compared to the original data (upper diagram). Only the 7:2 coordination after two o'clock is notably diminished. The course of Δq (black lines), which is per se identical for the original and the surrogate data, may explain this finding. During many periods of apparent coordination with k = 3 and α = 0.03, bivariate signal variation is low. Thus intermittent phase coordination most likely results mainly from a central adjustment of heart rate and respiratory rate but not from real beat-to-beat phase synchronization.

Figure 7

Dependence on age. There is a remarkable decrease of mean PP with age (upper diagram) which results from a loss in autonomic modulation of heart rate: Decreased RSA leads to lower detection rates of coordinated sequences by binary pattern analysis and to an inaccuracy of coordination analysis, e.g. expressed by lower overall correlation between phase and pattern recurrency (lower diagram, see also r₁ in Table 1).

Figure 8

Gray-scale maps with k = 15 (subject b05). Phase synchronization should be revealed when solely longer periods (e.g. >20 seconds) of phase coordination are taken into consideration. To check for synchronization, the stability condition was set at k = 15 heartbeats which led to the sparse gray-scale maps in this figure. Only the 7:2 phase and RSA pattern recurrences seem to partially meet the strong synchronization criteria which supports the findings above (see legend of Fig. 6).

Figure 9

Intermittent phase coordination and binary pattern recurrence. Upper two panels: Real data example of a 7:2 phase recurrence, i.e. the relative phases of R waves between successive onsets of inspiration (II interval) recur after seven heartbeats and two respiratory cycles, and at least over a period of k = 3 heartbeats. This is denoted as 7:2 intermittent phase coordination. Lower panel: The corresponding RR tachogram shows a pronounced RSA which is synchronous with the thermistor signal and which leads to a cyclical 7-bit RSA pattern recurrence (of pattern class 22, see Fig. 10), i.e. binary values are repeated after seven heartbeats over a period of at least k = 3 heartbeats. This is equivalent to the rotation of the heart rate acceleration pattern 0110010 → 1100100 → 1001001 → 0010011 → etc.

Figure 10

Example of a 7:2 phase locking pattern. A periodic and dominant frequency modulation of the heartbeat causes a predominance and cyclical recurrence of typical binary patterns of heart rate acceleration (1) and deceleration (0), as sketched here for a 7:2 phase-locked sinus modulator. The figure shows how a sinusoidal frequency modulation leads to a displacement of equidistant R peaks. The direction is indicated by arrows on top of the R peak bars. During inspiration (increase of idealized thermistor curve) R peaks are advanced and during expiration R peaks are retarded. The binary values below indicate the corresponding lengthening (0) or shortening (1) of RR intervals from one beat to the next. The two alternatively resulting patterns (0110011 and 0110010) belong to the same pattern class (the 7:2 phase locking pattern class which is designated as class 22 in [19,20]). In the range from 3:1 to 6:1 or 6:2 to 12:2, phase locking pattern predominance most likely originates from intermittent cardiorespiratory coordination as the binary constellations of these patterns correspond to high frequency heart rate variations, i.e. RSA. These classes are therefore denoted as RSA pattern classes without claiming a one-to-one correspondence to real cardiorespiratory synchronization. This relationship between RSA pattern predominance and cardiorespiratory synchronization is subject of the present study.