Spontaneous beat-to-beat variability of the ventricular repolarization duration

Spontaneous Beat-to-Beat Variability of the Ventricular Repolarization Duration

Giandomenico Nollo, MS,* Giorgio Speranza, MS,* Renato Grasso, MD,*+ Rodolfo Bonamini, MD,t Lucia Mangiardi, MD,+ and Renzo Antolini, PhD*+

Abstract: The spontaneous beat-to-beat variability of the ventricular repolarization duration was investigated in 2 1 healthy subjects (age 25-7 1 years; mean, 40 years) during the basal state in a recumbent position. For each subject, approximately 1,000 consecutive cycles were analyzed with an automated technique. The time series of the RR, QT, and RT intervals generate histograms that approximate normal distributions and have mean standard deviations of 57.0 ms, 5.4 ms, and 4.3 ms, respectively. Spectral analysis was used to detect rhythmical oscillations in these time series. The power spectra densities of both heart rate and ventricular repolarization during show peaks in the same frequency bands: low frequency (0.05-0.12 Hz) and high frequency (0.2-0.4 Hz). The power distribution between these two bands observed in the ventricular repolarization duration spectra was found to be the reverse of that in heart rate spectra (p < 0.005). Key words: ventricular repolarization duration, QT interval variability, spectral analysis, heart rate, autonomic tone.

The QT interval reflects on the surface electrocardiogram (ECG) the overall duration of the ventricular depolarization and repolarization processes. The QT interval is an important diagnostic measurement in clinical practice because its prolongation has been associated with pathological conditions. ’ However, even in the normal population, the duration of this interval is not constant, but changes mainly under the influence of two physiological factors: heart rate2-5 and autonomic nervous system (ANS) tone.‘,’

Since Bazett,’ many efforts have been made to define the limits of normality by characterizing the RRQT relation in normal population and many formulas have been proposed. “*’ ’ These formulae are based on empirical considerations and statistical analysis, but in none of them were the direct influences of the autonomic nervous system taken into account, nor was the dynamic behaviour of the QT interval duration adequately described.2 Anatomical and physiological studies evidenced differences in the innervation of the atria1 and ventricular myocardium, l2 as well as regional inhomogeneities within the ventricles. ’ 3.I4 These findings suggest that neural control could produce a different short-term regulation of the sinus node discharge than ventricular repolarization duration. Methods capable of evaluating the dynamic behavior of the QT interval, by characterizing its beatto-beat variations, may help to shed light on this

From the *Lstituto Ricerca Scientifica e Tecnologica (IRST) Trento, tlstituto Malattie Cardiovascolari Universitd di Torino, and #Dipartimento di Fisica Universitd di Trento. Italia.

Supported by the Istituto Trentino di Cultura (ITC) of Trento and by the Consiglio Nazionale delle Ricerche (CNR) through the Gruppo Nazionale di Cibemetica e Biofisica (GNCB). Reprint requests: Dr. G. Nollo, Medical Biophysics Area, IRST, I-38050 Povo (Trento), Italy.

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Journal of Electrocardiology

Vol. 25 No. 1 January 1992

complex control system. In fact, in recent literature, some authors proposed spectral analysis of heart rate variability signal as a noninvasive method to study cardiovascular regulation’5-17 and particularly to quantify sympathovagal interaction on the sinus node.‘8-20 In the present work, the same methodological approach was applied to investigate the ventricular repolarization during and the authors report on: ( 1) beat-to-beat measurement of approximately 1,000 consecutive RR and QT intervals, during steadystate, in supine healthy subjects by using an automated technique already described and validated”; (2) characterization of time series of the measured intervals through their mean values, standard deviations, and interval occurrence histograms; and (3) frequency-domain analysis of the ventricular repolarization duration time series to verify the presence of periodic components in its variability as already observed for the heart rate.

Materials

and Methods

Subjects Twenty-one healthy volunteers ranging in age from 2 5 to 7 1 years (mean, 40 years; 6 women and 15 men) were included in this study. Requirements for participation were: absence of clinically evident disease, nonsmokers, normal weight, normal rest ECG with normal QTc interval, normal arterial pressure, and absence of drug use and alcohol abuse. All subjects were carefully instructed about the study and all gave their informed consent.

ECG Recordings All ECGs were recorded using a five-channel ECG recorder (ESAOTE EP12), in the same laboratory, at comparable environmental conditions, between 3:30 and 4:00 PM at least 2 hours after a light lunch. Two channels were used for continuous monitoring, while the others allowed periodical checking of the standard 12-lead ECG. Input filters were set to obtain a signal bandwidth from 0.03 to 400 Hz. Subjects were placed in supine position, spontaneous breathing. After 10 minutes of stabilization, all output signals were stored on an FM tape recorder (TEAC MR 30, bandwidth DC: 1.25 KHz, SNR 48 dB) for 20 minutes. Arterial blood pressure and standard 12-

lead ECG were monitored during recording by an experienced cardiologist and were normal in all cases.

ECG Analysis For the analysis, the precordial lead V5 was chosen because of its stability and its high signal-to-noise ratio (SNR) . In normal populations, characteristic features of this lead are an evident Q wave, a positive monophasic R wave, and a wide T wave. An automatic measurement method was then developed and optimized to work on signals of this morphology. ECGs were played back from the tape, and the signal of the chosen lead was digitized at 1,000 samples/s with 12-bit resolution and stored on a 386 Compaq personal computer. An algorithm already described and validated2i was able to detect beat-tobeat the following fiducial points: QRS onset (Q), R wave maximum (R), T wave end (T). The Q detector was based on the study of the first derivative of the low-pass filtered signal. R was obtained by defining an approximate R position by a derivative threshold and then using a template matching criterion. The template was obtained as the mean of several QRS complexes. The matching was performed by a modification of the Cross-Differences algorithm,22,23 called Adapted Cross-Differences technique.21 T searching is activated within a properly defined temporal window whose limits are proportional to the preceding RR interval. Within this window, the signal is processed by a linear phase low-pass filter and differentiated (FIR, 100 coefficients, 20 Hz cutoff frequency, 45 dB rejection). T occurrence is estimated at the crossing of a threshold proportional to the derivative maximum. Beat-to-beat identification of QR, R, and T allowed us to obtain RR, QT, and RT interval series. The reproducibility of the measurement was carefully tested by computing the standard deviation of the estimates as a function of SNR. The SNR was computed for each ECG recording as the ratio between the total cycle and the diastolic phase root mean square, both normalized by baseline value subtraction. To keep the standard deviation of the QT estimate within 3 ms, recordings with SNR < 15 were rejected.2’

Interval Series Analysis In the presence of an isolated extrasystolic beat (EB), the corresponding RR, QT, and RT values in the interval series were substituted with the mean

Beat-to-Beat Variability of QT Interval between pre-ectopic and post-ectopic values.” In the study subjects, EBs were very rare and other kinds of arrhythmias were absent. For each interval series, mean value, standard deviation, and the intervals occurrence distribution were calculated. Power spectral density (PSD) was estimated by using the Welch periodogram method, which requires the calculation of an averaged PSD on overlapped. segments of data. After a mean value subtraction, a Blackman window was applied to each segment to reduce the effect of the side lobes and to decrease the estimation bias, at the price of a slight decrease in resolution. Overlapping increases the number of segments for a given data record length, decreasing the PSD estimate variance,24 which is (assuming segment independence) roughly inversely proportional to the number of averaged segments. It can be shown that the estimate variance is reduced by a factor greater than that obtained using consecutive segments.25 With 50% overlap of 256 point segments, at least 5 segments for the averaging procedure were obtained in each series considered. Since the spectral analysis was carried out on interval series, the spectra obtained are functions of “cycle per beat.” The frequency scale of each spectrum was then corrected in “cycle per second” dividing by the mean value of RR intervals. In accordance with the literature,‘7*26 three regions of the spectra were considered: very low frequency band (VLF) characterized by frequencies less than 0.05 Hz, Table Subject

1.

l

Nollo et al.

11

low-frequency band (LF) including frequencies from 0.05 Hz to 0.15 Hz, and high-frequency band (HF) covering the frequency range from 0.2 Hz to 0.4 Hz. As proposed in recent literature on RR variability series, ‘9,20 the ratio between low-frequency and high-frequency components power (LFP/HFP) has been evaluated as a characteristic index even for the RT series.

Results Two examples of RR, QT, and RT interval time series and their corresponding occurrence histograms are shown in Figures 1 and 2. Figure 1 was obtained from case 1 characterized by the highest SNR value, Figure 2 was obtained from case 8 with the lowest SNR value. Histograms of each series display a regular shape that approximates a normal distribution. Statistical parameters computed on each interval time series are reported in Table 1. Depending on the individual heart rate, the number of analyzed cardiac cycles varies from 898 to 1,618 (mean 1,27 1). The mean standard deviation (SD) of RR interval duration is 57.0 ms, in accordance with data reported in literature. I9 The mean SDS of the QT and RT interval durations were 5.4 ms and 4.3 ms, respectively. These SDS, corrected for the mean value of the corresponding interval (SD%), show that relative spon-

Time Domain Statistical Values

Sex

Age

NI

(RR)

SD

SD%

(QT)

SD

SD%

(RT)

sn

SD%

SNR

M M M M M F M M M M M F F F F M M F M M M

40 53 71 32 28 29 53 34 58 25 34 33 52 49 60 26 28 27 35 29 43

1005 1309 1258 1488 1167 1065 1275 1347 898 1129 1169 1099 1589 1347 1076 1140 1186 1559 1618 1540 1437

1003 892 936 791 1006 913 935 872 1311 1044 1007 998 740 874 1093 825 993 753 728 751 819

54.0 42.0 20.0 60.0 75.0 39.0 56.0 77.0 37.0 79.0 49.0 65.0 24.0 32.0 38.0 125.4 60.6 75.3 66.7 62.3 60.2

5.7 4.7 2.1 7.6 7.5 4.3 6.0 8.8 2.8 7.6 4.9 6.5 3.2 3.7 3.5 15.2 6.1 10.0 9.2 8.3 7.4

355 375 442 355 406 442 422 393 463 370 435 392 346 386 411 364 407 366 350 359 364

2.9 4.7 6.9 5.7 7.9 7.0 7.0 6.3 4.6 2.1

320 333 41 I 309 350 396 393 348 419 326 392 346 304 351 373 315 354 318 304 309 320

2.5 3.4 5.9 5.1 5.4 4.4 4.9 5.9 3.4 1.0 4.8 2.9 5.1 4.4 2.6 6.4 3.4 6.6 4. I 5.0 3.3

1.1 1.0

3.8 7.4 4.8 6.1 6.5 3.9 6.8 4.4 5.2 3.7

1.1 1.3 1.6 1.6 1.9 1.6 1.7 1.6 1.o 0.6 1.5 1.0 2.1 1.2 1.5 I .8 1 .o 1.9 1.3 1.5 1.0

1.7 1.5 1.1 1.2 1.7 0.8 0.3 1.2 0.8 1.7 1.3 0.7 2.0 1.o 2. I I.3 1.6 1.0

48.62 25.44 21.97 21.91 34.86 25.02 18.58 16.29 27.97 23.27 20.54 24.04 25.25 16.38 23.12 22.66

918

57.0

391

5.4

1.41

347

4.3

1.27

1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Mean values

NI = number SD% = standard

6.43

6.4

of analyzed intervals; ( ) = mean interval value (ms); SNR = signal-to-noise deviation corrected for the mean value of the corresponding intervals.

ratio:

SD = standard

I .3

deviation

47.64

21.09 30.66 45.44 23.41 26.86 (ms);

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Journal of Electrocardiology

Vol. 25 No. 1 January 1992

RR series 60 A

1300

1 50 P) ; 40

1200 :

1100

5

1000

E

it k 30 i=f ; 20

"Tl

900

800

0 10

700 { I

I

I

I

I

200

0

I

I

I

I

I

I

600

400

I

I

I

0

I

800

1000

700 600 900 1000110012001300

(msec)

beats

(RR) = 1003

SD = 54

QT series 400 $

';; 360 j

360 -

5

340 320 -

1

I

I

I

0

I

I

I

200

I

I

I

400

I

I

I

I

I

1

l(10’0

600

600

(msec)

beats

SD = 2.9

(QT) = 355 RT series 360 7

340

2

320

z

300 260 I

0

I

I

I

200

I

I

I

I

I

400

I

600

I

I

I

600

I

I

1000

(msec)

beats (RT) = 320 Fig. 1. RR, QT, and RT interval value (ms); SD = standard

series and corresponding deviation (ms)

occurrence

histograms.

Case 1 with highest

SD = 2.5 SNR. ( ) = mean

Beat-to-Beat

Variability

of QT Interval

Nollo et al.

l

13

RR series 1200I

600 - 4 T,

,

I

I

I,

400

I

I

I,

600

I

a00

I

I,

I

I,,

1000

I

1200

I

I

1400

a00

600

beats

(msec) (RR)

= 072

SD = 77

QT series 440

,

I

420 z : 400-

if! a0 k

E - 380 -

I

::

E

0

360 -

60

4o 20

340

II, 0

I,, 200

I,, 400

I,,

I,,

a00

600

I,, 1000

I,, 1200

1400

340

360

beats

380

400

420

4

0

(msec) (QT) = 393

SD = 6.3

RT series 400

140 120

380 --0 ," 360E

:

100

5

a0

340 -

k 1

60

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:: 0

40

z

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300

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200

I I I, 400

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I I I, I I I, I I I, I I I a00 1000 1200 1400

0

,“‘,“‘,“‘,“‘[“~ 300

320

beats

360

380

400

(msec) (RT)

Fig. 2. RR, QT, and RT interval series and corresponding value (ms); SD = standard deviation (ms).

340

occurrence

histograms.

= 348

Case 8 with lowest

SD = 5.9 SNR. ( ) = mean

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Journal of Electrocardiology

Vol. 25 No. 1 January 1992

variations are higher for RR intervals than for QT and RT intervals. SNR was always higher than 16.3. In this range of values, in accordance with a previous study,28 QT and RT variabilities are comparable, particularly considering SD%, showing that the heart rate-dependent QT variation is mainly localized in the repolarization phase. As far as the spectral analysis of the ventricular repolarization duration is concerned, both QT and RT interval series spectra are superimposable in case of high SNR. The QT spectrum shows an additional large band white noise contribution due to the difftcult estimate of Q wave fiducial point. Therefore, the following spectral analysis were performed on the RT interval series. PSD estimates of RR and RT interval series in cases 1 and 8 are presented in Figure 3. The spectra were obtained using the Welch periodogram with 50% overlapping of consecutive segments of 256 intertaneous

vals. The PSD of RR intervals series confirms the al-

ready observed power distribution in three well-defined frequency bands. The same characteristic distribution is also evident in the PSD of RT interval series. This behavior was observed in all cases, even in the presence of the worst SNR (Fig. 3B). Table 2 summarizes the results of the spectral analysis of both RR and RT interval series indicating the numerical values of the power collected in each frequency band. The LFP/HFP ratios, which in the analysis of RR variability have been considered an index of sympathovagal balance, are also reported for both series. LFP and HFP values relative to RR interval series are comparable with values from normal populations found in the literature.‘9,20,30 A remarkable result emerging from this spectral analysis is the reversal of the LFP-HFP balance in RT series with respect to RR series. Since the data does not allow a RR PSD

RR PSD 100

,, 0.1

0.2

0.3

0.0

0.4

0.1

I,,,

0.3

I,,,

0.4

I 5

0.4

0.5

(Hz)

frequency

(Hz)

frequency

,*, 0.2

RT PSD

RT PSD 25

011

0:2

frequency

013

0.4

0.0

0.5

0.1

0.2

frequency

(Hz)

0.3

(Hz)

6 Fig. 3. Power spectral density of RR and RT interval series obtained through the Welch periodogram computation. (A) case 1. (B) case 8. Even in case of worst SNR (case 8). the RT power spectral density shows the characteristic three-band distribution as for RR power spectral density.

Beat-to-Beat Variability Table 2.

VLFP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

BP

435.61 1229.08 2148.67 226.65 994.34 579.71 832.75 1311.84 462.34 1591.61 404.33 408.89 2331.98 13 30.06 573.04 5 12.46 436.12 455.00 197.45 226.08 644.23

Mean

825.34

Values

RT Spectral

LFP

HFP

LFPIHFP

380.9 1 138.22 88.06 336.59 272.74 216.44 155.01 137.96 61.20 93.32 227.30 140.35 166.24 148.14 142.66 372.42 194.05 442.28 486.65 574.39 194.59

134.40 128.51 82.60 191.76 255.79 187.66 152.52 72.81 12.42 84.88 206.01 103.43 157.08 124.70 100.27 118.97 120.46 420.21 64.95 427.31 87.14

240.44 6.51 4.92 48.46 8.97 21.34 1.37 49.49 47.61 6.52 12.51 28.90 7.81 18.76 40.63 133.95 67.13 7.70 386.20 76.38 73.40

0.56 19.75 16.80 3.96 28.53 8.79 111.34 1.47 0.26 13.02 16.47 3.58 20.12 6.65 2.47 0.89 1.79 54.54 0.17 5.59 1.19

236.64

153.99

61.38

15.14

VLFP

VLFP = very low frequency band power (CO.05 Hz); LFP = low frequency power (0.2-0.4 Hz): BP = band power (>0.05 Hz).

parametric evaluation, a x2 test was performed to assess the statistical significance of this finding by choosing a threshold value of 1 for the LFP/HFP ratio. In Table 3, the x2 2 x 2 contingency table confronts the number of values of the considered LFP/HFP ratios above and below this threshold and shows the significance of the difference (p < 0.005).

Discussion While several studies have been published on the beat-to-beat variability of the heart rate and blood pressure, little is known about the variability of the QT interval duration. This work reports the analysis of RR, QT, and RT interval series obtained from a population of normal subjects in steady-state conditions. Measurements were performed beat-to-beat on the V5 lead sampled at a rate of 1,000 sample/s by using recognition algorithms, which make a

Table 3. 2

x 2 Contingency Table of RR and Power Spectral Density LFP/HFP Values LFP/HFP

RR RT p < 0.005.

Nollo et al.

l

15

Power Spectral Density (Welch Periodogram) Parameters of RR and RT Interval Series RR Spectral

Subject

of QT Interval

< 1

4 14 Abbreviations

LFP/HFP

> 1

17 7 same as in Table 2.

Total

RT

Subjects 21 21

Values

BP

LFP

1095,69 282.23 48.76 145.64 277.2 1 302.16 178.25 905.37 46.7 I 66.82 111.15 612.39 49.65 43.38 82.10 2041.13 1873.59 416.68 496.16 158.76 43.60

133.25 112.15 249.33 195.39 195.56 196.37 133.42 85.18 188.49 280.34 205.9 1 94.53 256.50 286.38 187.38 72.90 31.04 164.31 286.75 207.00 405.75

12.72 22.59 57.58 30.28 56.45 12.41 35.11 27.70 49.16 48.40 43.55 43.36 62.17 57.06 56.29 26.26 17.99 88.61 25.08 89.61 76.54

I1 5.79 55.91 136.96 115.2 1 119.70 154.74 75.82 19.90 84.36 133.95 95.63 33.48 60.04 129.45 105.33 25.13 8.59 30.12 244.99 68.24 222.94

0.11 0.40 0.42 0.26 0.47 0.08 0.46 I .39 0.58 0.36 0.46 1.29 I .04 0.44 0.53 1.05 2.09 2.94 0.10 1.31 0.34

409.09

191.73

46.31

96.02

0.80

band power

(0.05-O.

HFP

LFPIHFP

15 Hz); HFP = high frequency

band

choice between samples, for the fiducial point time occurrence estimate, with a consequent resolution of 1 ms. The resulting QT and RT interval series have, in all subjects, very low SD (QT: mean SD = 5.4 ms; RT: mean SD = 4.3 ms). This finding emphasizes the need of a high-performance measurement system to study the beat-to-beat variability of these electrocardiographic intervals. Spectral analysis was performed on the RT interval series, since the smaller error in the measurement of RT intervals is associated with a reduction of the random component in the PSD, while deterministic components are more evident, improving the readability of the spectra. In absence of critical modification of the QRS complex, the spontaneous beatto-beat variability of the ventricular repolarization duration is then better estimated by using RT interval series. The spectral analysis of RT variability reveals that PSD is predominantly distributed in distinct frequency bands. In all cases, both in RR and RT spectra, the power concentrates within the same frequency bands already found in all studies on heart rate variability. The interest of the study has been particularly focused on LF and HF bands, whose power is commonly considered an index of the influences of the two limbs of the ANS in the shortterm regulation of the heart rate. The last finding leads one to suppose that the same control mechanism regulates heart rate and RT in-

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Journal of Electrocardiology

Vol. 25 No. 1 January 1992

in basal conditions. Dependence of RT interval duration on heart rate has been the object of many clinical and experimental investigations since the beginning of electrocardiography, leading to the concept of corrected QT ( QTc).However, the significance of the inversion of the LF-HF balance seen in the RT series PSD with respect to the corresponding RR series PSD cannot be justified by taking only into account the classical RR-QT relationship. A physiological explanation of this finding is beyond the purposes of this work and requires further investigation. Two hypotheses, however, may be suggested. Some authors indicate that the LFP/HFP ratio is an index of the sympathovagal balance on the sinus node as reflected in heart rate variability. If the same meaning could be transferred to the LFP/HFP ratio computed from RT PSD, then a different ANS control operating on the sinus node discharge and the ventricular repolarization duration may be postulated. Anatomical and physiological studies that point out differences between atria1 and ventricular innervation may support this hypothesis.‘2-14 Recent studies conducted on monophasic action potential (MAP) recorded from the human ventricle indicate that the relation between MAP (or QT interval) and heart rate does not depend only on the preceding RR interva1.2’4 A memory effect leading to a more complex, nonconstant RR-QT relationship has been postulated to explain those experimental results. A nonconstant, frequency-dependent, RRQT relationship could also explain the reversal of the LF-HF balance on RT PSD observed in this work. The two mechanisms are not mutually exclusive. They may act alone or together, giving rise to a very complex control system. In conclusion, the beat-to-beat analysis of the RT interval revealed the presence of deterministic components even in the variability of this ECG interval. Such new cardiovascular variability signal has an amplitude only one tenth of that already known for the RR and requires a very accurate measuring system to be detected. Existing technologies permit its detection and may offer a powerful tool to investigate the dynamic behavior of the ventricular repolarization, particularly its short-term regulation mechanism. An impairment of such regulatory processes can be related to imbalances of the ANS input to the heart. This phenomenon is the suspected underlying mechanism of severe and sudden ventricular arrhythmic events characteristic of a number of diseases, for example, long QT syndrome, sudden infant death syndrome, post-infarction, diabetic neuropathy, and CNS diseases. While static QT and/or QTc prolongation were proposed by some authors as a terval variability

marker of risk for arrhythmic complications during the course of the above-mentioned diseases, their power in discriminating populations at different risk appeared weak and is still controversial. This fact suggests that markers of other electrophysiological phenomena playing the major role in the genesis of arrhythmias should be sought. The dynamic behavior of the QT interval in response to the continuous changes of the heart rate is the direct expression of the ventricular repolarization duration adaptability. A quantitative estimate of such adaptability, in terms of its weakness or lateness, might be related to the risk of sudden ventricular arrhythmias. The spectral analysis of the RT interval variability could be a methodoloy suited to this purpose.

Acknowledgment The authors are grateful to Prof. B. Taccardi for reading and commenting on the manuscript.

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