INTRODUCTION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

IN ACUTE MYOCARDIAL INFARCTION (AMI) patients, thrombolysis using streptokinase or recombinant tissue plasminogen activator (rtPA) is a widely used procedure. The occurrence and timing of reperfusion have crucial clinical implications, both for prognosis and for subsequent treatment. Reperfusion is typically determined by examining the changes in the ST segment and in the pain as reported by the patient. The principal aim of this study was characterizing the patterns of heart rate (HR) fluctuations, which appear in synchrony with reperfusion. Such characterization would reveal the behavior of the autonomic nervous system (ANS) during AMI and may lay the basis for a HR-based marker of reperfusion, which would be independent of current markers.

The basic concept that underlies this study is the activation of cardiovascular reflexes by altered myocardial blood supply. It has been shown in animal models that myocardial ischemia induces strong autonomic reflexes: left anterior wall ischemia tends to induce sympathetic activation (43, 54), whereas ischemia of the inferoposterior wall tends to induce parasympathetic activation (23, 54). These findings have been corroborated in humans too (1, 2, 23, 54). The bimodal autonomic activation has been observed in humans during AMI: anterior wall MI (AW-MI) was found to stimulate mostly sympathetic activity, whereas inferior wall MI (IW-MI) was shown to stimulate marked parasympathetic activity (23, 54, 59). Alterations of cardiac control have been observed after reperfusion (4) and were correlated with mortality (37).

In this research, we studied the effect of alteration in myocardial perfusion on autonomic cardiac rate control by applying a continuous time-dependent analysis to HR fluctuations, focusing on the several minutes before and after reperfusion.

Cardiac autonomic activity cannot be directly investigated in clinical scenarios. Therefore, a noninvasive tool, which does not interfere with the system under study, is needed. Such a tool, although not without drawbacks, is the spectral analysis of HR variability (HRV). The typical spectral pattern of HRV includes two main spectral peaks: a low-frequency (LF) region, reaching up to 0.18 Hz, affected by both sympathetic and parasympathetic activity, and a high-frequency (HF) peak centered at the respiratory frequency, which is associated with parasympathetic activity (3, 6, 33).

Standard approaches to the spectral analysis of HRV are based on the assumption of steady-state conditions (47). This assumption discards any dynamics in the power spectrum. Therefore, standard approaches are inapplicable when examining nonstationary conditions, as expected in the case of thrombolysis. Moreover, the lack of dynamics in the standard analysis is profoundly inconsistent with the intrinsically dynamic nature of the thrombolysis procedure. Several approaches have been used to cope with the need to assess a time-varying power spectrum. The short-time Fourier transform (18, 47), time-dependent autoregressive models (16, 18), Wigner-Ville distribution (18, 19, 44, 46), discrete wavelet transform (DWT; Ref. 21), the Hilbert transform (10, 11, 22, 32, 36, 41, 51, 58), and the selective-discrete-Fourier transform algorithm (SDA) (7, 34, 35) have been used to obtain time-dependent spectral analysis.

In this study, we apply a special form of the continuous wavelet transform (CWT; Ref. 55). This wavelet transform inherits many features of the SDA, which was developed in our laboratory (34). The CWT of the HR signal performs a time-frequency decomposition of the signal and yields a time-dependent version of the typical LF and HF peaks. Hence, this approach provides an insight into the dynamics of cardiac autonomic control, as reflected by HRV.

As described below, the application of the wavelet transform to HR traces obtained during thrombolytic therapy reveals the rich and complex dynamics of autonomic activity, in relation to reperfusion, and in some cases to reocclusion.

	METHODS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

Patients and Clinical Conditions

The patients were classified according to the location of the infarct: IW-MI and AW-MI. MI location was determined by the Minnesota Code (20) as follows: anterior (V₁ through V₅), inferior (L₂, L₃, or aVF), or lateral (L₁, aVL, or V₆). ECG was recorded during thrombolytic therapy (streptokinase or rtPA) from the bedside monitor (Horizon 1000, Mennen Medical) to an analog magnetic tape (FM recorder, XR-30 cassette data recorder, TEAC) and later digitized at a sampling rate of 500 Hz at a 16-bit resolution (MP100, Biopac).

Recordings started on admission to the Intensive Cardiac Care Unit (ICCU) and lasted for 3 h. Oral remarks concerning therapeutic interventions, pain described by the patient on a scale of 0-10, and ST elevations were recorded by the physician on the audio channel of the tape and later extracted digitally. We were therefore able to synchronize the events with the ECG. The occurrence and timing of reperfusion were determined by the attending physician: the criterion was a reduction of 50% in ST elevations compared with ST elevations at admission. The time resolution of detecting a 50% change in ST elevation is ~1 min. R-waves were detected offline, and corrections were made for arrhythmias.

The criteria for inclusion in this study were that 1) reperfusion was determined according to the aforementioned criteria; 2) reperfusion occurred during the recording session; 3) opiates were not administered before reperfusion; 4) medications were not administered 1 h before reperfusion; and 5) patients exhibited only infrequent and isolated arrhythmias, which were corrected offline.

Based on the above criteria, 17 patients were included in this study. Nine patients (7 men, 2 women, age 55.8 ± 12.0 yr) were diagnosed with AW-MI, and eight patients (7 men, 1 woman, age 65.9 ± 11.1 yr) were diagnosed with IW-MI. Relevant information about the patients is available in Table 1. One IW-MI patient (T28 in Table 1) exhibited two events of reperfusion, separated by an event of reocclusion. Seven patients were on chronic therapy with -blockers, and five patients received -blockers in the ICCU (3 of those patients were on chronic therapy with -blockers). One patient was on chronic therapy with an ₂-agonist drug (T28 in Table 1). Two patients received atropine (T28 and T26 in Table 1), yet the fact that a significant HF peak was observed in both cases, as well as the administered dosage (8), negates the possibility of full cholinergic blockade. Reocclusion, manifested by increase in ST elevations and chest pains, was detected in four recordings.

Because the interpretation of HRV indexes and the mathematical analysis are not performed in the typical manner, we choose to elaborate on those issues in the following two subsections.

Physiological Interpretation of HRV

The LF peak has more complex relations with the ANS because it is affected by both the sympathetic and parasympathetic activation (3). Again, as for the HF peak, sympathetic denervation, either surgical (14, 15, 56, 57) or pharmacological (6, 8, 31), results in a markedly reduced, yet still measurable, LF peak. In the case of sympathetic denervation, the LF peak is probably caused by the mechanical effect of LF fluctuations in the arterial pressure (50, 56). Intense sympathetic activation, such as during an exercise test (15, 49), results in a diminished LF peak, similar to the one found in the denervated system. During an exercise test, the increase of sympathetic activity in response to an increased workload is first manifested by an increase of the LF power, which then decreases abruptly as the workload further increases (49).

Changes of the LF peak cannot be interpreted independently of the HF peak. Therefore, when associating changes in the LF and HF peaks to changes in ANS activity, one must consider the effect of the sympathetic and vagal activities on the indexes of HRV. A change in vagal activity is reflected by a parallel change in both the LF and HF peaks, when the sympathetic activity does not change (5, 6). However, a change in sympathetic activity is reflected mainly by a change in the LF peak (3, 5, 6, 13, 52).

Therefore, the effect of alterations in autonomic activity on the LF and HF peaks can be summarized as follows: 1) a change in vagal activity without a significant change in sympathetic activity is reflected by a parallel change in both the LF and HF peaks; and 2) opposite changes of sympathetic and vagal activities can be reflected by a change in the HF peak either without any change in the LF peak or with an opposite change in the LF peak.

The second pattern can be identified by inspecting the ratio between the LF and HF peaks. This ratio has been claimed to reflect the sympathovagal balance under a variety of physiological conditions (17, 45, 48). Yet, another hurdle complicates this analysis. Although the sympathetic and parasympathetic systems usually act in opposing directions, the two systems may change their activity concordantly (38), and indeed, overactivity of both systems has been observed during AMI (59). Therefore, we expect that overactivity of both the sympathetic and vagal branches would result in an increase of both the LF and HF peaks, while the LF peak should increase more than the HF peak. This kind of dual increase would result in an increase of the ratio between the LF and HF peak.

Another issue, which must be addressed, is the intricate autonomic response to inhibitory and excitatory provocations. Stimulation of the autonomic system, aimed at increasing HR, can induce either an increase in sympathetic activity or a reduction in vagal activity, or both. Similarly, HR reduction is mediated by either sympathetic withdrawal or an increase in vagal activity, or both. We categorized the various HRV responses to reperfusion into two classes, which reflect the excitatory and inhibitory stimulations, either of which may be induced by changes in myocardial perfusion. These classes comprise the complex relations between the ANS activity and HRV indexes. We categorized the changes of the LF and HF peaks into two classes (class 1 and class 2), assuming no change in respiratory rate or tidal volume has occurred.

Class 1. The changes in HRV parameters comply with one of the following patterns, indicating a shift in balance toward relative vagal enhancement. 1a) LF decreases and HF is unchanged or increased, indicating a reduction in sympathetic activity; vagal activity is unchanged or increased, depending on the change in HF. 1b) LF is unchanged and HF increases, indicating a reduction in sympathetic activity and an increase in vagal activity. 1c) Both LF and HF increase, but their ratio is unchanged or reduced, indicating increased vagal activity and unchanged or reduced sympathetic activity, respectively. 1d) Both LF and HF decrease and their ratio decreases, indicating decreased vagal and sympathetic activities, with a shift in balance toward relative vagal enhancement.

Class 2. The changes in HRV parameters comply with one of the following patterns, indicating a shift in balance toward relative sympathetic enhancement. 2a) HF decreases and LF increases or is unchanged, indicating a reduction in vagal activity and increase in sympathetic activity. 2b) LF increases and HF is unchanged, indicating increased sympathetic activity and unchanged vagal activity. 2c) Both LF and HF decrease, and their ratio is unchanged, indicating reduction of vagal activity without considerable change of sympathetic activity. 2d) Both LF and HF increase and their ratio increases, indicating increased vagal and sympathetic activities, with a shift in balance toward relative sympathetic enhancement.

Mathematical Analysis

The main difference between the above-mentioned time-frequency decomposition methods and the CWT lies in time resolution and frequency resolution. Time resolution (t) is the minimal time difference between two events that are differentiable using a specific time-frequency method. Any two events that are closer than t would be detected as one event. Accordingly, frequency resolution (f) is the minimal detectable frequency difference between two spectral peaks. The length of the windows directly determines the time resolution: short windows provide high time resolution, whereas long ones impose low resolution. Interestingly, in all time-frequency decomposition methods (including the CWT), time and frequency resolutions are linked according to the Heisenberg uncertainty principle: tf  2, which means that high time resolution imposes low frequency resolution and vice versa. Although this shortcoming cannot be circumvented, the CWT presents a unique solution to it using a variable window length. In the CWT and the SDA, the duration of the time window is inversely proportional to the frequency of interest, unlike most methods of time-frequency decomposition

(1)

Thus it is long for low frequencies and short for high frequencies. Combining the Heisenberg uncertainty principle and the proportion rule in Eq. 1 results in frequency-dependent time and frequency resolutions of the time-frequency decomposition of a signal

(2)

Therefore, the CWT has high frequency resolution (small f) with low time resolution (large t) in the LF range and low frequency resolution with high time resolution in the HF range.

In contrast to the Fourier transform, which expresses a signal as a sum of waves (sines and cosines), the CWT expresses a signal as a sum of limited-duration waves, termed wavelets. The shape of those wavelets is determined by the wavelet function (t), which defines the CWT. The definition of the general CWT of a signal s(t) is (21)

(3)

where W_CWT(t,f) is the CWT at time t and frequency f, and the superscript asterisk denotes the complex conjugate. As our wavelet function, we chose the complex sum of a sine and a cosine

(4)

where the factor k is a parameter of analysis (the superscript H stands for harmonic). Inserting Eq. 4 into Eq. 3 provides the explicit form of the CWT that we used

(5)

Equation 5 means that W(t,f), which is the CWT of s(t) at time t and frequency f, is the Fourier transform of the product of s(t) with a time window centered at time t and of duration k/f. Thus |W(t,f)|² provides a time-frequency decomposition of the signal. The power in frequency bands (such as the HF and the LF bands) can be assessed by integrating |W(t,f)|² over the desired frequency band. A more detailed mathematical analysis is presented in the APPENDIX.

Computation of HRV Parameters

Time-dependent spectral analysis of the HR traces was performed with the wavelet transform using Eq. 5. The respiratory frequency band was detected from the wavelet transform of the HR signal (see, e.g., Figs. 2 and 3). The time-dependent power of the HF peak, HFP(t), was computed using Eq. 9 (see APPENDIX), by setting the limits of integration according to the respiratory frequency band. The time-dependent LF peak, LFP(t), was computed using Eq. 9, by setting the frequency limits of integration to 0.04 and 0.18 Hz. This procedure is exemplified in Fig. 1.

View larger version (25K): [in this window] [in a new window]   Fig. 1.   Simulated heart rate (HR) trace and the associated analysis procedure. Graphs show (top to bottom) the HR signal (simulated; a), the Wavelet transform |W(t,f)|2 coded as grayscale (b), the time-dependent low-frequency (LF) peak LFPm(t) (c), the time-dependent high-frequency (HF) peak HFPm(t) (d), and their ratio R(t) (e). The mean HR does not change in this example; however, HR variability (HRV) parameters change considerably: the LF component decreases at t = 0, and the HF component increases. The HF component changes its frequency from 0.35 to 0.5 Hz at t = 300 s. The wavelet transform enables one to pinpoint the time of change in the spectral pattern of the HRV. Integrating the wavelet transform over specific frequency bands [LFP(t) and HFP(t)] provides a time-dependent quantitative estimate of HRV power spectrum. BPM, beats/min; LFPm(t) and HFPm(t), median-filtered versions of LFP(t) and HFP(t).

Both resulting traces, the time-dependent LFP(t) and HFP(t) traces, were then filtered, using a moving median filter 3 s long to reduce short-term outliers. The median-filtered versions are denoted by LFP_m(t) and HFP_m(t), respectively. Their ratio, R(t) = LFP_m(t)/HFP_m(t), was computed (see Fig. 1). This ratio reflects the dynamic time-dependent version of the widely used LF/HF ratio. In our analysis, we considered four time-dependent parameters: HR(t), LFP_m(t), HFP_m(t), and R(t). These parameters reflect the dynamics of HRV and HR control.

Data Analysis: Reperfusion

The difference between the average values of the four parameters during the two epochs was computed, for example, as follows for the change in heart rate (_HR)
where the angled brackets denote taking the average. The changes in LFP_m(t), HFP_m(t), and R(t), i.e., _LF, _HF, and _R, were similarly computed.

In addition, we computed the SD of each of the four parameters in the 3,000-s epoch centered around the reported time of reperfusion: _HR, _LF, _HF, and _R. The difference in the four parameters (_HR, _LF, _HF, _R) between the two epochs was compared with their corresponding SD (_HR, _LF, _HF, and _R, respectively). A change in each of the parameters was considered significant when the difference was larger than the SD: e.g., |_HR| > _HR. In other cases (_{HR  HR  HR}), the change in the parameter was considered nonsignificant.

As a result, each parameter was classified as one of three options: increase, decrease, or unchanged at reperfusion. This approach prevents random changes of the parameters from being considered as real changes.

We examined this approach by considering the traces of 15 patients. In each patient, we select, at random and well before diagnosed reperfusion, a time of "false reperfusion." We then chose two epochs: before the false reperfusion and after the false reperfusion, exactly as we did around the time of true reperfusion. If, as we claim, our statistical test has high specificity, then it should not detect significant changes in the HRV parameters and/or in the HR between the two epochs (before and after the false reperfusion). Applying the test to the LFP_m(t), HFP_m(t), and R(t) using these paired epochs around the false reperfusion time resulted only in a single detection of significant change (false positive) in each of the HRV parameters (P < 0.001). These three misdetections of the HRV parameters occurred all in one single patient (T02 in Table 1). However, applying the same test to the HR data of all 15 patients resulted in the detection of a significant change in 5 patients (P > 0.15). Therefore, we conclude that the test we chose has a low rate of type 1 errors (false positive) for the HRV parameters (6%) and that this test is not reliable for the HR signal. We discuss the type-2 error rate (false negative) of this test in the DISCUSSION.

Each reperfusion event was classified into one of the two classes mentioned in the previous section, namely, vagal or sympathetic relative enhancement, according to the combined change in LFP_m(t), HFP_m(t), and R(t).

The relative change (D) in HR and HRV parameters was expressed as the percent change between the level of the parameter after reperfusion and the level before reperfusion, computed as follows, for example, for HR
The relative changes in LFP_m(t), HFP_m(t), and R(t), i.e., D_LF, D_HF, and D_R, were similarly computed.

The instantaneous respiratory rate was determined from the wavelet transform of the HR trace (see Figs. 2 and 3). At every instant t, respiratory rate is the frequency at which |W(t,f)| is maximal within the HF range of frequencies. SD of the respiratory rate was also evaluated. Changes in respiratory rate were considered significant when the difference was larger than the SD (similarly to the analysis of HR and HRV parameters).

View larger version (32K): [in this window] [in a new window]   Fig. 2.   Wavelet analysis of patient T30 [anterior wall myocardial infarction (AW-MI)]. See Fig. 1 for explanation of graphs. Reperfusion, marked by the dashed vertical line, was clinically detected at t = 2,900 s. Notice the marked decrease of LFP(t) (by 95%), HFP(t) (by 78%), and R(t) (by 70%) starting at t = 2,900 s. HR reduced by 6 beats/min (7%). The HFP(t) increased gradually while R(t) decreased. The change of HRV parameters suggests a biphasic shift in cardiac autonomic activity toward parasympathetic enhancement: the 1st transition, which involved reduction of sympathetic and parasympathetic (although to lesser extent) activities, is concomitant with reperfusion, followed by a slow increase of parasympathetic activity.

View larger version (41K): [in this window] [in a new window]   Fig. 3.   Wavelet analysis of patient T26 [inferoposterior wall myocardial infarction (IW-MI)]. See Fig. 1 for explanation of graphs. Reperfusion, marked by the dashed vertical line, was clinically detected at t = 1,400 s. Notice the marked increase of LFP(t) (by 380%) and R(t) (by 480%) starting at t = 1,280 s. The HFP(t) decreases slightly (7%). HR reduces by 2 beats/min, which is <2%. The increase of LFP(t) can be clearly seen in the time-frequency decomposition of the HR signal (2nd panel from top). The change of HRV parameters suggests a shift in cardiac autonomic activity toward sympathetic enhancement.

Data Analysis: Reocclusion

Statistics

	RESULTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

Changes in HR and HRV Parameters at Reperfusion

HR exhibited significant changes in 14 of 18 reperfusion events. The absolute value of relative change in HR (|D_HR|) was 5.3% ± 4.4 (n = 18).

Among the nine AW-MI patients, HR increased in seven cases (only 5 were significant) and decreased in two cases (only 1 was significant). Among the nine IW-MI events, HR increased in four cases and decreased in five cases (HR decrease was not significant in 1 case). The classification of HR change according to MI location was not significant (P > 0.3, Fisher exact test). No correlation was found between the changes of HRV parameters and MI location: ANOVA of LFP_m(t), HFP_m(t), and R(t) with respect to MI location and time of reperfusion (the epochs BR and AR) did not exhibit any significant effect.

However, an important observation was made: the type of HRV alteration (class 1 or class 2) was unequivocally associated with the respective MI location. Individual responses of HRV parameters are detailed in Table 1.

Classification According to MI Location

HRV responses classified as class 2 were observed in three AW-MI patients and in eight episodes of reperfusion found in seven IW-MI patients (P < 0.03, Fisher exact test). An example of the analysis of HR fluctuations for an IW-MI patient (T26 in Table 1) during reperfusion is shown in Fig. 3. In this case, reperfusion was detected at t = 1,400 s. Clinical reperfusion was preceded by a marked increase in both LFP_m(t) (380%) and R(t) (480%), followed by an additional monotonic increase of these two parameters. The HFP_m(t) increased only slightly (9%). This behavior complies with class 2b. The HR decreased only slightly (2 beats/min; 2%).

Interestingly, we found no correlation between the class of HRV response and the HR response (P > 0.2, Fisher exact test; cases in which HR was not changed significantly were excluded from the statistical test).

Roughly summarizing, we might say that the class 1 response to reperfusion occurs mainly in AW-MI, whereas the class 2 response is dominant in IW-MI.

Paradoxical Relation Between HR and HRV

Figure 4 shows an example of this phenomenon, obtained from patient T14, with IW-MI. The shutdown occurred at t = 3,500 s: average HR decreased by 7 beats/min accompanied by a 90% reduction in both LFP_m(t) and HFP_m(t). On the other hand, the turn-on of HR fluctuations occurred 70 min later at t = 7,700 s: an increase of HR by 2 beats/min was accompanied by an increase in both LFP_m(t) and HFP_m(t) (485 and 4,160%, respectively).

View larger version (36K): [in this window] [in a new window]   Fig. 4.   Wavelet analysis of patient T14 (IW-MI). The left panels show the HR and the wavelet transform of the HRV at t = 3,300-3,800 s, whereas the right panels show the same parameters at t = 7,500-8,000 s. The HR reduction (7 beats/min, 9%) at t = 3,500 was accompanied by a marked decrease of both LFP(t) and HFP(t) (90% each), usually associated with a parasympathetic withdrawal. Conversely, at t = 7,750 s, HR increase (2 beats/min, 3%) was accompanied by marked increase of both LFP(t) (485%) and HFP(t) (4,160%), usually associated by strong parasympathetic activation.

In the other case, which exhibited a similar phenomenon (T02, again an IW-MI), a HR decrease of 19 beats/min was accompanied by a reduction in HFP_m(t) by 73%. Later, a HR increase of 6 beats/min was accompanied by an increase in HFP_m(t) by 368%.

Reocclusion

View larger version (29K): [in this window] [in a new window]   Fig. 5.   Wavelet analysis of patient T28 (IW-MI). This patient had an eventful thrombolysis: reperfusion was diagnosed at t = 1,500 (dashed line), followed by reocclusion at t = 2,000 (dotted line) and then a 2nd reperfusion at t = 3,200 (dashed line). The 1st reperfusion was accompanied by a marked reduction of HFP(t) (95%) and an increase of R(t) (3,680%). HR decreased by 3 beats/min (4%). Reocclusion was accompanied by an increase of HFP(t) (660%) and decrease of R(t) (55%). HR increased by 6 beats/min (9%). The 2nd reperfusion was accompanied by a reduction of HFP(t) (96%) and an increase of R(t) (640%). HR decreased by 9 beats/min (14%). In this case, the spectral pattern of HRV clearly complies with the perfusion of the heart.

Respiratory Rate

	DISCUSSION

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION APPENDIX REFERENCES

The principal result of this study is the finding of marked alterations in the HRV components in relation to myocardial perfusion. While the average changes in HRV parameters were not significantly different between the two study groups, a case-by-case examination detected substantial alterations. By itself, this result is clinically important, because it indicates that continuous assessment of HR fluctuations may provide a marker of reperfusion, independent of the currently used markers. As for the type of changes found in the HRV patterns, these can be explained in terms of alterations in cardiac autonomic control of heart rate.

In this study, we link the time-dependent change in the spectral pattern of HRV to changes in activity of the ANS. It is known that stimulation of sympathetic afferents causes either one or both of the following reactions: increase of firing rate of sympathetic efferents and reduction of firing rate of vagal efferents (25, 29, 38, 39, 53). Therefore, the mutually opposed effect of sympathetic (or parasympathetic) stimulation has to be accounted for when interpreting the changes in the power of the spectral components of HRV. On the other hand, MI may stimulate a parallel activation of the sympathetic and parasympathetic systems (59). Such complex responses of the vagal and sympathetic systems to ischemia are most probably the reason for the lack of correlation between the average changes of HRV parameters and MI location in this study.

The classification of HRV response into the two classes, described in METHODS, enabled us to incorporate several complex autonomic responses into a coherent picture. Indeed, we found a significant correlation between the location of the infarct and the type of autonomic activation (class 1 or class 2) at reperfusion (and reocclusion), as reflected by HRV. In terms of the parameters of HRV analysis introduced above, a shift in balance toward relative sympathetic enhancement (class 2) was found in all nine reperfusion episodes of IW-MI patients (the reperfusion episodes of the patients who exhibited the paradoxical HR-HRV correlation are discussed in the following subsection). The AW-MI patients presented a less uniform behavior: six of nine patients exhibited a shift in balance toward relative vagal enhancement (class 1).

Other studies, focusing on the time before reperfusion, have also found that the proportion of IW-MI patients exhibiting vagal dominance (and therefore expected according to our hypothesis to shift toward relative sympathetic enhancement after reperfusion) is higher than the proportion of AW-MI exhibiting sympathetic dominance (4, 40, 59). The proportions in those studies were lower than the ones we found, namely, 100% for IW-MI and 66% for AW-MI.

It is important to note that we do not speak of vagal or sympathetic overactivity, as referred to in several studies (40, 59, 60), since we did not assess the baseline ANS activity. Moreover, our hypothesis deals only with changes related to alteration of myocardial perfusion. This restrictive assumption stems from the diversity of clinical conditions, which we encountered in this study: some patients received medication directly affecting their baseline HRV, whereas others were hypertensive and were medicated accordingly. Yet, we assume that 1 h after taking any medication (see inclusion criteria), its concentration is in steady state, and therefore any changes in HRV can be attributed to changes in ANS activity rather than to the effect of the medication.

Surprisingly, the changes of HR correlated neither with HRV, nor with MI location. Correlations may become statistically significant in a larger patient population, yet even at this stage, it is clear from the magnitude of HR changes and from the directions of the changes, that HR itself is a poor marker of reperfusion. This finding conflicts with previous studies, which used the HR to study ANS activity during myocardial ischemia and infarction (24, 40, 59, 60). However, considering in our case the diversity of the patients, in terms of initial clinical condition, the chronic use of medications and the medications given during the thrombolysis (especially -blockers), it is reasonable to assume that HR is determined not only by the ANS, but by other factors.

Paradoxical Correlation between HR and HRV

The second observation is crucial for the understanding of this phenomenon, because if such an intense change in ANS activity occurred, it could explain the pattern of HRV. Yet, the occurrence of such an intense alteration of ANS activity is contradicted by two facts. Reperfusion, which might have induced such an alteration, did not occur at that stage and in fact occurred much later in both patients. Second, mean HR changed only slightly during the abrupt HRV changes, and, moreover, the HR changed in the direction opposite from that predicted by the type of HRV alteration. By itself, the slight and opposing HR change does not prove, but only supports, the lack of an intense autonomic stimulation, because we found in the current study that in many cases, mean HR did not correlate with HRV. We explain this result in terms of vagal saturation, of either the cholinergic receptors in the SA node, or of the nerve fibers. Vagal saturation has been observed by Goldberger and coworkers (26-28) in normal subjects under -blockade, in which vagal activity was gradually increased using phenylephrine. In those studies, the gradually increased vagal activity resulted in a monotonic HR reduction and a gradual increase of the HF peak. However, at a certain level, the HF peak decreased dramatically, whereas HR continued to decrease. This phenomenon has been attributed to saturation of the vagal system.

In this view, when the vagal system is highly active, and its level is close to the saturation threshold, a slight increase of vagal activity causes the mean HR to slightly decrease but may drive the SA node into a state of saturation, which inhibits the fluctuations of the HR caused by modulation of vagal activity. In that case, only the mechanically induced fluctuations remain effective. When the vagal activity is reduced to a level below the saturation threshold, then mean HR slightly increases and HRV parameters may exhibit a two-order-of-magnitude increase.

In the case of IW-MI patients, such explanation is highly plausible. Therefore, under vagal saturation, a small and gradual reduction in vagal activity results in an abrupt increase in the HF peak. In both patients who exhibited the saturation phenomenon, both the LF and HF peaks increased after reperfusion. This increase in the HF peak reflects a reduction of vagal activity, and the increase in the LF peak suggests an increase in sympathetic activity, i.e., a pattern of shift in balance toward relative sympathetic enhancement.

Incorporating this modification of HRV interpretation to the classification of HRV patterns according to MI location indicates that all of the nine reperfusion episodes of IW-MI patients were associated with a shift in balance toward relative sympathetic enhancement (P < 0.001, Fisher exact test).

Impact of Respiratory Rate

In contrast, patient T02, in whom respiratory rate decreased, exhibited paradoxical HR-HRV relations. In this case, the HF peak is determined mostly by the mechanical coupling in the cardiorespiratory system, which is less influenced by changes in respiratory rate, as shown by Bernardi and coworkers (14).

Reocclusion

Mathematical Analysis

In general, when implementing such a threshold approach, fine-tuning of the threshold value and/or of the trace duration used for the assessment of the intrinsic level of noise of the relevant HRV parameter should be performed according to the specific experimental or clinical setup. In our case, when applying it to subtraces where no reperfusion had occurred (see METHODS), we found that this threshold test is very reliable for the HRV parameters in terms of type-1 error (false positive detection rate). Therefore, we may conclude that the changes in HRV parameters considered as significant according to this test most likely reflect true physiological changes, in this case, reperfusion or reocclusion. The correlation between the location of the infarct and the type of HRV response confirms that this test also has low rates of type-2 error, because the specific HRV changes were closely correlated with changes in myocardial perfusion. Our test was found unreliable, leading to many false-positive detections when applied to the HR signal. This finding is in accordance with the lack of correlation between the HR response and the infarct location, indicating that this test applied to the HR has also high false-negative detection rate.

Focusing on the dynamic changes, we were also able to observe events of shutdown and turn-on of the HRV, which probably reflect saturation effects of the vagal system. Overlooking this effect would increase the intersubject variability of the measured parameters, thus masking the true behavior of ANS activity. In addition, the continuous analysis enabled us to observe autonomic changes related to reocclusion.

Limitations of the Study

The clear correlation between the types of HRV response to reperfusion (class 1 and class 2) and the location of infarct (IW-MI or AW-MI) indicates that the effect of changes in tidal volume that occur at reperfusion is insignificant, relative to reperfusion itself. This assumption is also supported by the fact that respiratory rate, as reflected by the frequency of the HF peak, did not change considerably in most patients, thus lowering the possibility that respiratory volume may have changed.

Conclusions

The CWT, although more complicated mathematically than standard spectral analysis, provides a rich description of the time-dependent evolution of HRV and the autonomic control branches involved during extreme physiological conditions and in a complex clinical setting.