Heart Care Info - Heart Disease Prevention & Treatment

Decreased neuroautonomic complexity in men during an acute major depressive episode: analysis of heart rate dynamics

SJJ Leistedt, P Linkowski, JP Lanquart... - Translational ..., 2011 - Abstract Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Increasing evidence supports the conceptual framework that complex ... [HTML]

From the European data format (edf) files, the ECG signal was extracted and converted to open source WFDB format (http://www.physionet.org). An automated QRS detection algorithm was then used to detect beats and annotate them as either normal sinus or ectopic.39 Outliers due to missed or false beat detections were identified using a sliding window average filter. Intervals 0.4 s or greater than 2.0 s were excluded from the window average. Next, using a window of 41 intervals, the average over the window was calculated, excluding the central interval. If the central interval was outside 20% of the window average this interval was excluded and the window advanced by one interval.

From the resulting beat annotation files, we calculated the following standard time domain HRV statistics: the average of all the normal sinus to normal sinus (NN) intervals (AVNN), the s.d. of all NN intervals (SDNN), the s.d. of the averages of NN intervals in all 5-min segments (SDANN), the mean of the s.d.'s of NN intervals in all 5-min segments (SDNNINDX), the root mean square of consecutive differences between adjacent NN intervals (r MSSD), and the percentage of adjacent intervals whose difference is higher than 10 ms (p NN10), 25 ms (p NN25) and 50 ms (p NN50)40 (http://www.physionet.org).

The following standard frequency domain measures were calculated using the Lomb periodogram for unevenly sampled data: total spectral power (TOTPWR; 0–0.4 Hz), ultra-low frequency power (ULF; 0–0.003 Hz), very-low frequency power (VLF; 0.003–0.04 Hz), low frequency power (LF; 0.04–0.15 Hz), high frequency power (HF; 0.15–0.4 Hz), the ratio of low to high frequency power (LF/HF) and the slope (β) of the spectrogram on a log-log scale assessed over the range of 0 to 0.04 Hz.

SDNN and TOTPWR are measures of variance. SDANN is a measure of the degree of non-stationarity of the time series. SDNNINDX quantifies how much the variance changes over time. r MSSD and p NN measures quantify HF fluctuations. Therefore, these time domain measures, in addition to HF power, have been used as indexes of cardiac vagal tone modulation. LF power is thought to reflect both sympathetic and vagal influences. LF/HF ratio has been proposed as an index of cardiac autonomic control but it is no longer widely accepted as a direct indicator of ‘sympatho-vagal balance.’ ULF and VLF powers quantify nonspecific trends in the time series. The β exponent is one measure of the fractal scaling properties of a signal. For HR time series obtained from healthy subjects under baseline conditions, this exponent is usually close to −1, which indicates the presence of long-range correlations.41, 42

In order to quantify the dynamical complexity of the RR interval time series, we used the MSE method described in detail elsewhere.15, 17 Briefly, the MSE method quantifies the degree of irregularity of a signal using an entropy measure, such as Samp En, over multiple time scales. Samp En31 is the negative natural logarithm of an estimate of the conditional probability that subseries (epochs) of length m that match pointwise within a tolerance r will also match when the length of each of these subseries increases from m to m+1 data points. Signals that are highly irregular, and therefore more entropic, over a wide range of scales are more complex than both those that are highly regular, that is, periodic, and those that are irregular only at a single time scale (for example, white noise). The line obtained by connecting the Samp En values (y axis) for a range of time scales (x axis) is called the MSE curve. We then derived a short-term and longer-term complexity index (CI).

As noted, in traditional HRV analysis, the cutoff separating the low and high frequency bands is 0.15 Hz, which corresponds to a period of ~7 s. Physiologically, the high frequency band encompasses HR fluctuations associated with respiration. To quantify the complexity of the dynamics over a comparable high-frequency band, we define a short-term CI as the area under the MSE curve ranging from scales 1 to 8, inclusive (Given the fact that the average HR for both groups was about 60 beats per minute, the mean RR interval was ~1 s and, therefore, scale n corresponds to ~n seconds). We note that the specific cutoffs for both traditional frequency and complexity analyses are somewhat arbitrary.

To probe the complexity of the dynamics on relatively longer time scales, we computed a CI defined as the area under the MSE curve from scales 1 to 20. The upper cutoff scale chosen (20), although necessarily arbitrary, is based on previous studies of HR time series.17, 30 Standard parameter values for calculating Samp En are as follows: m=2 and r=15% of the time series’ s.d.'s.31, 32 Here, we chose m=2 and r=8 ms for the following reason. The parameter r determines the level of noise accepted. If xi−xi+1 >r, then the two data points, xi and xi+1, are distinguishable. If instead, xi–xi+1 r, the two data points are indistinguishable, that is, their difference is considered noise, not signal. As the ECG recordings were sampled at 250 Hz, each RR interval is determined with an uncertainty of 4 ms (1/250), and the difference between RR intervals with an uncertainty of twice this value. To be above the noise level we chose r=8 ms. Qualitatively similar results were obtained using r=15% of the time series’ s.d.'s, corresponding to r values ranging from 6 to 18 ms.

The number of time scales one can probe depends on the total length of the original signal. Analysis of larger time scales requires longer signals. As a rule of thumb,32 at least 200 consecutive data points are needed for reliable calculation of Samp En. Therefore, if N is the length of a signal and S the largest time scale to be included in the MSE analysis, the relationship between the two variables should be such that N/S>200. For the analysis of HR dynamics during different sleep stages, we considered 15 min or longer segments with at least 1000 data points, and computed Samp En for time scales 1 to 5, inclusive. The analysis of full-night HR dynamics is not constrained by the length of the time series, which ranged from about 23 000 to 40 000 data points. Therefore, in this case, we were able to compute a CI encompassing entropy values for both short (1 to 8) and longer (1 to 20) time scales.

Mann–Whitney non-parametric U-tests were used to examine group (controls versus depression) differences in clinical, demographic, and sleep measures as well. This test was deemed most appropriate because of the sample size and data distribution. To adjust for age, we also fit least squares regression models. To evaluate the association between MSE and depression during specific sleep stages, we fit linear regression models. Because there were multiple observations per participant, we used generalized estimating equations methods with an exchangeable correlation structure to account for within-participant correlation. Spearman correlation was used to assess the association between MSE and the Hamilton depression scale among the depressed patients. All analyses were performed with a (type I error) set at 0.05 using the SAS statistical software (version 9.13 for Windows, SAS Institute, Cary, NC, USA).

Standard PSG measures are summarized in Table 2. Consistent with previous reports,43, 44 the depression versus healthy groups showed statistically significant decreases in sleep efficiency, total sleep time, rapid eye movement latency, percentage delta sleep, and increases in sleep onset latency, percentage stage 1 sleep, awakenings throughout the night and in rapid eye movement density.