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European Journal of Heart Failure 2002 4(6):737-743; doi:10.1016/S1388-9842(02)00167-8
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© 2002 European Society of Cardiology

Time and frequency analysis of beat-to-beat R–T interval variability in patients with ischaemic left ventricular dysfunction providing evidence for non-neural control of ventricular repolarisation

Maciej Sosnowski*, Zbigniew Czyz and Michal Tendera

Third Division of Cardiology, Silesian Medical School Ziolowa 47, 40-635 Katowice, Poland

* Corresponding author. Tel./fax: +48-32-2523930. E-mail address: msos{at}poczta.onet.pl


   Abstract
Top
 Abstract
1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
 References
 
Background: Determinants of temporal lability in ventricular repolarisation are not fully recognised. We aimed to analyse the sources of RT variability by comparing normal subjects and patients after myocardial infarction (MI) with either depressed or preserved left ventricular (LV) function.

Methods: One hundred and nine patients (27 women, 82 men, aged 51±9 years) were divided into three groups: 24 patients (pts) with an uncomplicated angiographically proven coronary heart disease (CHD-group), 59 post-MI pts with preserved LV function (LVEF>40%, PMI-N-group) and 26 post-MI pts with depressed LV function (LVEF<40%, PMI-L-group). An ECG signal of low-noise 512 heartbeats was recorded using a computer-assisted amplifier (16 bit, 2 kHz). The onset and offset of the R-wave and T-wave were determined automatically. The magnitude of R–R and R–T variability was measured as the standard deviation of all intervals (SD–RR and SD–RT, ms, respectively). Their relationship was quantified by the correlation coefficient rRT/RR. Power spectral density of RR or RT variability was estimated with the FFT (Welch's averaged periodogram, Hanning window) and frequency relation was quantified using a squared coherence spectrum (SCS). For all spectral and cross-spectral measurements two frequency ranges were considered: high (0.15–0.50 Hz, HF) and low (0.04–0.15 Hz, LF). Spectral power and SCS of RR and RT variability for both ranges (HFRR, LFRR, HFRT, LFRT, SCSHF, SCSLF), and the ratios LF/HFRR and LF/HFRT were drawn for comparisons. The central frequency of HFRR was considered as the frequency of respiration (fresp, Hz).

Results: In the PMI-L group the SD–RT was significantly greater compared to the remaining groups and accounted for almost 10% of the SDRR. Also, the coefficient rRT/RR was weakest in this group. The spectral indices of RR variability were similar in all groups, while the greatest value of the HFRT was observed in the PMI-L group. The SCSLF was insignificant in this group, contrary to the CHD and PMI-N groups. Additionally, there were significant negative relationships between fresp and spectral indices of RT variability in PMI-patients with depressed LV function.

Conclusion: A greater beat-to-beat variation in RT interval duration along with increased power of its HF component indicates an important role of respiration in ventricular repolarisation control, while reduced time- and frequency RT–RR relationships seem to relate to an impaired process of ventricular duration adaptation.

Key Words: Ventricular repolarisation • Power spectrum • Coherence spectrum • Myocardial infarction • Ventricular function

Received December 7, 2001; Revised February 12, 2002; Accepted May 20, 2002


   1. Introduction
Top
 Abstract
 1. Introduction
2. Materials and methods
 3. Results
 4. Discussion
 References
 
Abnormal ventricular recovery from excitation plays an important role in the genesis of ventricular arrhythmias and in some cases may lead to sudden arrhythmic cardiac death [1–5]. Recognition of this abnormality from surface electrocardiogram could be simply achieved by the measurement of QT interval, and its prolongation increases risk of fatal arrhythmic events [1–3]. Abnormal recovery, however, may exist in the presence of normal QT duration in the resting ECG [6]. More recently, a great interest has been focused on spatial dispersion of QT interval, although the difference in QT duration between different electrode placement had been observed by Burdon-Sanderson and Page as early as 1883 [7]. Thus, another pattern of abnormal ventricular recovery is represented by increased spatial dispersion of QT duration [8]. Several studies clearly showed that increased spatial QT dispersion may be treated as an index of high risk for sudden cardiac death [9–12].

Quite modern approaches to quantitative evaluation of ventricular repolarisation are based on computer-assisted ECG analysis and include measurement of subtle T-wave alternans [13], precordial T-waves sequence analysis [14], and temporal dispersion of QT, i.e. measurement of beat-to-beat differences in duration of QT interval from short-time or 24-h ECG recordings [15–18]. However, there are only few reports on prognostic significance of increased temporal QT lability in high risk patients [19,20]. In the meantime, an analysis of QT interval (or more strictly RTapex or RTend) variability is focused on the possible explanation of its physiological and pathophysiological determinants. This goal can be achieved by means of such procedures as head-up tilt, controlled breathing or fixed heart rate [21–23]. We decided to examine the determinates of RTend variability by comparing patients with uncomplicated coronary heart disease (CHD) with post-MI patients with preserved or depressed left ventricular (LV) function.


   2. Materials and methods
Top
 Abstract
 1. Introduction
 2. Materials and methods
3. Results
 4. Discussion
 References
 
2.1. Study population
One hundred and nine patients were randomly selected for this study from a large population of patients presenting with stable angina pectoris on admission to our institution between March 1993 and May 1998. They represented approximately 1% of the entire patient population. There were 27 women and 82 men, aged 51±9 years (range 27–73). All patients were in sinus rhythm without premature ventricular or atrial beats during ECG recordings. Based on medical history and echocardiographic examination (Sonos 2500, Hewlett-Packard, USA) patients were divided into three groups: 24 pts with angiographically documented coronary artery disease without history of myocardial infarction (MI) (CHD-group), 59 pts after MI with preserved left ventricular ejection fraction (LVEF>40%, PMI-N group) and 26 pts after MI with LVEF<40% (PMI-L group). Clinical characteristics of these groups are presented in Table 1. Each examination was performed between 10.00 h and 12.00 h, at least 1 h after breakfast. Prescribed medication was allowed according to clinical indications. The study was performed according to the Declaration of Helsinki. All patients were informed about the purpose and methods of study, and gave their consent orally.


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  		Table 1 Baseline characteristics of patients with uncomplicated CHD, post-infarction patients with (PMI-N) and without (PMI-L) LV dysfunction

 
2.2. Signal recording and processing
The study was performed with the patient in a supine position during spontaneous breathing, after at least 10 min rest, using orthogonal X, Y, Z leads. Silver/silver chloride electrodes were placed according to established recommendations [24]. An ECG signal was gained using a computer-assisted low-noise amplifier at 0.03–600 Hz band (–3 dB). The low-noise amplifier allowed elimination of DC and long-wave noises. Analog-to-digital conversion was performed with 16 bit resolution and 2 kHz of sampling rate per channel (Kardioassist v.3.0, Medea Corp., Gliwice, Poland). After initial matched filtering for the R-wave detection (high pass true-linear filter) the noise level was calculated automatically on 81 ms lasting window placed at the T–P segment. Only the periods including consecutive 512 sinus beats with a raw noise level<0.02 mV were chosen for further processing. Fiducial points of the R-wave and of the offset of T-wave were automatically determined in the lead in which the amplitude of T-wave was maximal, but never less than 0.25 mV, and simultaneously the noise level was minimal. Such criteria allowed the optimal signal-to-noise ratio (SNR) for the T-wave to be obtained.

Final determination of the R-wave and T-wave fiducial points was accomplished using fourier shift method. These procedures allowed precise determination of the R-wave and T-wave fiducial points even in the presence of an unfavourable SNR (i.e. for SNR=10 dB). The standard error of fiducial point determination in the presence of muscle noises was 0.0868 ms for R-wave and 0.5803 ms for T-wave [25].

2.3. Data analysis
Time-series of R–R intervals was constructed as absolute beat-to-beat interval duration, while time-series of R–T intervals were calculated as beat-to-beat R–T duration differences from the reference value, which was drawn automatically from the template of the median R–R interval. Thus, for each consecutive sinus beat a positive or negative change from the reference was measured. The absolute values of the R–T interval duration were ignored.

The magnitude of R–R and R–T variability was assessed in time-domain as the value of standard deviation of mean R–R or ‘reference-attributed’ R–T interval (SD–RR and SD–RT, ms, respectively). Graphic presentation of RR–RT interval relationship was obtained and quantified by correlation coefficient rRT/RR (Fig. 1). Power spectral density was estimated with the FFT using Welch's averaged periodogram method. The signal was divided into 16 sections of 64 values overlapped on each other for 50% of their length. Each section was detrended and weighted with the Hanning window. Data were averaged to obtain spectral estimate of the entire signal. Corresponding spectra of R–R and R–T variability are presented in Fig. 1. The cross-spectrum between R–R and R–T variability was quantified using a squared coherence spectrum (SCS). The SCS indicates the fractional portion of the mean-square value of the second signal (considered as the output) that is contributed to the first signal (the input) at certain frequency. The SCS always ranges between 0 and 1, where 1 indicates the total correlation between two signals. SCS was calculated in each segment and the averaged maximum value was obtained (Fig. 1).


Figure 1
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  		Fig. 1 Graphic presentation of R–R and R–T variability in the time-domain (upper panel) and in the frequency-domain (lower panel).

 
For all spectral and cross-spectral measurements two frequency components were considered: high-frequency (0.15–0.50 Hz, HF) and low-frequency (0.04–0.15 Hz, LF). Spectral power and SCS of both components of R–R and R–T variability signals (HFRR, LFRR, HFRT, LFRT, SCSHF, SCSLF), as well as the ratios LF/HFRR and LF/HFRT were drawn for comparisons. For statistical purposes the values of spectral power were transformed into natural logarithm. Additionally, the central frequency of HF component of R–R variability was determined in order to obtain the respiratory frequency (fresp, Hz).

2.4. Statistics
Data are presented as mean±1 S.D. unless otherwise indicated. The Mann–Whitney U-test, Kruskal-Wallis ANOVA and {chi}2- or median-tests were used for comparisons, as appropriate. Correlations between parameters of R–R and R–T variability were quantified using Spearman rank test (CSS Statistica, Tulsa, OK). Differences were considered significant at P-level<0.05.


   3. Results
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
4. Discussion
 References
 
Raw data of each group are presented in Table 2. In PMI with compromised LVEF (PMI-L) the magnitude of R–T variability was significantly greater than in the other groups. SD–RT accounted for a greater percentage of the total R–R variability, as indicated by significantly greater value of NSD–RT that reached almost 10%. The correlation coefficient of time-dependent RT/RR relationship, although significant, was weakest in this group. The spectral indices of R–R variability were similar in all groups, while the greatest value of the spectral power of high frequency component of R–T variability was observed in the PMI-L group. The lowest, however, not significantly different, value of the LF/HF (RT) ratio was observed in this group. Mean SCS value at low frequency did not reach statistical significance (<0.5), contrary to those observed in the CHD and PMI-N groups.


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  		Table 2 Means of analysed indices of R–R and R–T variability

 
The results of the analysis of relationships between R–R and R–T variability measures are presented in Table 3. The magnitude of R–T variability showed no significant correlation with RRI and SD–RR or with spectral indices of R–R variability in all groups. In the CHD-group, significant correlations between power of high frequency components of both variability signals were observed. In both groups of post-MI patients, no significant correlations between the power of corresponding R–R and R–T spectral components were found. In all groups there were statistically significant positive relationships between SCS and the power of R–R variability at corresponding frequency (high or low), but only in the PMI-L group was a positive correlation observed between SCS at low frequency and the power of the high frequency component of R–R variability and a positive significant relationship between the LF/HF ratio of R–T variability and the power of LF component of R–R variability.


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  		Table 3 Correlations between R–R and R–T variability indices in the examined groups

 
There were significant negative correlations between the central frequency of HF component of R–R variability and time or spectral indices of R–T variability, only in PMI-patients with compromised LVEF. The remaining measures of R–T variability did not correlate with the central frequency of HF component of R–R variability in all groups. Sympathovagal balance, as indicated by the value of LF/HF(RR), correlated in a different manner with the power of HF component of R–T variability and consistently negatively with the magnitude of SCS at high frequency, mainly in PMI-pts with low LVEF.


   4. Discussion
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
References
 
The first important observation is that the magnitude of R–T variability was significantly increased in post-MI patients with impaired LV systolic function. The mean value of SD–RT accounted for almost 10% of total R–R variability and was greater than in CHD-pts without MI and PMI-pts with normal LVEF. This result corresponds well with a significant increase of QT variability index in patients with dilated cardiomyopathy [19]. An analysis in the frequency domain showed that the main cause of such an increase is related to augmented power of the high frequency component. One possible explanation of such changes of R–T variability in patients after MI with low LVEF is that they are linked to the alterations in sinus cycle duration and its variability. The results of our study indicate that neither RRI nor SD–RR, nor spectral components of R–R variability were different among the groups compared. Thus, it is very unlikely that changes of the autonomic control of the heart contribute to the increase of R–T interval beat-to-beat variation. It has previously been shown, that in patients with CHD, especially those who recover from an acute MI, the vagal control of the heart is reduced, as compared to normal subjects [26–28]. The reduction of parasympathetic control is more obvious in patients with heart failure [29–31], and in end-stage heart failure is followed by cardiac desensitization [32]. If we assume that in our PMI-pts with an impaired LV the vagal control of ventricular repolarisation is similarly affected as the vagal control of the heart rate, we would expect reduced rather than increased beat-to-beat variation of R–T intervals. A sparse network of parasympathetic endings within the ventricles, contrary to richly innervated atria, also mitigate against the important role of the vagus in controlling the duration of ventricular repolarisation [33].

If we take into account the significant values of coherence spectra at high frequency range, however, a common mechanism is likely to be responsible for R–R and R–T variability. The only apparent candidate mechanism is respiration, or strictly its mechanical influences. The results of regression analysis of linear correlations between the frequency of respiration, drawn from the R–R power spectrum, and measures of R–T variability support such a hypothesis. Lombardi et al. observed high frequency changes of R–T variability during a fixed atrial rate that were unlikely to be explained as the result of changes in autonomic drive [21]. Porta et al. tried to explain R–T variability as the result of changes in electrical position of the heart within the chest with the respiratory cycle [34]. The dependence of RT variability on respiration was especially evident in post-MI patients with LV dysfunction. A similar observation was made by Fujimura et al. as marked increase in QT variability index in 41 patients with heart failure in response to controlled breathing at 10/min [35]. Thus, another possible explanation is respiratory-related changes in venous return and LV filling. An impaired LV is more sensitive to volume load than a normal ventricle, so the increase in variability of ventricular repolarisation duration may be a feature of mechanoelectric coupling within the failing ventricle [36,37].

The second interesting result of this study is that in post-MI patients with depressed LVEF, the relationship between R–R and R–T intervals was less marked than in the remaining groups. Sarma et al. [18] evaluated the relationship between beat-to-beat Q–T intervals and the average of five preceding sinus beats during 24-h Holter monitoring in 10 patients with angina pectoris and found no significant correlation. In our study, in patients with CHD and in PMI-patients with normal LVEF, however, the RT/RR relationship was moderate, but statistically significant (0.417 and 0.398, respectively, both n=512 points, P<0.001). Usually, such an analysis is performed on data averaged from 24-h ECGs or during exercise tests and brings a variety of regression formulae. This paper presents results obtained using short-term beat-to-beat analysis of the RT/RR relationship, as opposed to using averaged data. We are not surprised to find only moderate correlation between R–T and R–R intervals. The work of Franz [5] and Vainer et al. [38] showed a long-wave adaptation of Q–T duration to rapidly changeable sinus cycles. The relationship we found is less close than indicated by 24-h analysis or exercise testing, because of smaller range of R–R intervals screened to calculate the regression coefficients. The existence of long-wave R–T interval adaptation that is related to sympathetic activity is also demonstrated by the significant positive correlation between the SCS at low frequency and mean R–R interval and its standard deviation, since the latter are under control of autonomic balance shifted towards sympathetic predominance in resting conditions.

An explanation for the poor RT/RR relationship in the time-domain and a reduced SCS at low frequency range in the spectral-domain in PMI-patients with impaired LVEF, should take into consideration an abnormal or somewhat disturbed process of ventricular repolarisation adaptation. It is possible that abnormal adjustment of R–T duration to changeable sinus cycles is the result of electrical uncoupling. The latter has been already documented in patients after MI and in patients with heart failure [36].

In summary, increased beat-to-beat variation of R–T interval duration along with increased power of its HF component indicate an important role of the mechanical influences of respiration. At the same time a reduced RT/RR interval relationship and lower SCS at low frequency range both seem to indicate an impaired process of adaptation of ventricular repolarisation.


   References
Top
 Abstract
 1. Introduction
 2. Materials and methods
 3. Results
 4. Discussion
 References
 

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