Olivassé Nasario-Junior

Refining the deceleration capacity index in phase-rectified signal averaging to assess physical conditioning level.

BACKGROUND Deceleration capacity (DC) of heart rate is a measure of cardiac vagal modulation. This study introduced a DC adaptation (Modified Index) that measured the velocity of change in the phase-rectified signal averaging curve, and assessed its ability to discriminate athletes from controls. MATERIALS AND METHODS The Modified Index was compared to Standard DC approach in a prospective case-control study. Subjects were classified according to maximal metabolic equivalents as the control group (CG) and athlete group (AG). The Modified Index was compared to Standard DC and classical approaches (RMSSD and HF) by the area under receiver operating characteristic curve (AUC) using 10,000 bootstraps. RESULTS In Standard DC and Modified Index bootstrap median values were (ms), respectively, 11.80 and 17.94 (p<0.01) in CG, and 25.98 and 45.62 in AG (p<0.01). AUC (mean±SD) was 0.70±0.12 for Standard DC and 0.96±0.04 for Modified Index (p<0.01). CONCLUSIONS Modified Index appropriately discriminates athletes from healthy sedentary subjects.

The effect of configuration parameters of time-frequency maps in the detection of intra-QRS electrical transients of the signal-averaged electrocardiogram: Impact in clinical diagnostic performance

Time-frequency maps of signal-averaged electrocardiogram based on a short time Fourier transform (STFT) technique analysis was employed to assess the presence of high frequency electrical transients (turbulence) and arrhythmia risk assessment. The optimal configuration set of STFT variables aiming at risk stratification for sustained monomorphic ventricular tachycardia (SMVT) is still undetermined. Different configuration variables, including analyzing time-window widths, starting positions, relative displacements, and zero-padding for STFT time-frequency maps constructions were combined on each analyzing averaged signal from 18 healthy controls and 18 subjects presenting inducible SMVT. Spectral turbulence analysis (STA) was, thus, carried out according to conventional procedures. The optimal configuration set of variables for STA was obtained by assessing the total diagnostic accuracy of all combinations of parameters. The optimal diagnostic performance was found at 86% total diagnostic accuracy as compared to 56% using previous defined normality thresholds (p=0.01). Present configuration set of variables is distinctive from previously defined set of variables and improves risk stratification.

Effect of aerobic conditioning on ventricular activation

BACKGROUND The athletes heart represents a reversible structural and functional adaptations of myocardial tissue developed through physical conditioning. Surface electrocardiogram (ECG) has the capability to detect myocardial hypertrophy but has limited performance in monitoring physical conditioning-induced myocardial remodeling. The aim of this study was to develop an ECG-derived test for detecting incipient myocardial hypertrophy in well-conditioned athletes based on a principal components (PC) analysis. METHODS Two groups of study composed of 14 sedentary healthy volunteers (CONTROL GROUP) and 14 professional long distance runners (Athlete group) had their maximal metabolic equivalents (MET) estimated (mean ± SD: CONTROL GROUP 9 ± 2 METs vs. Athlete group: 20 ± 1 METs, p<0.05). All participants had their high-resolution ECG (HRECG) recorded, and a 120 ms segment starting at the QRS complex onset and ending in the ST segment was extracted to build a data matrix for PC analysis. The Mahalanobis distance was evaluated by a logistic regression model to determine the optimal separation threshold between groups. HRECG was also analyzed using the classical time domain approach. The comparison of areas under the receiver operating characteristic curve (c-statistic) in 10,000 bootstrap re-samplings measured how well each method detected physical conditioning (α<0.05). RESULTS Average bootstrap c-statistic for PC analysis and time domain approaches were 0.98 and 0.79 (p<0.05), respectively. PC analysis and maximal oxygen consumption exhibited comparable performances to distinguish between groups. DISCUSSION The PC analysis method applied to HRECG signals appropriately discriminates well-conditioned athletes from healthy, sedentary subjects.

Post-exercise heart rate variability recovery: a time-frequency analysis

Objective Most studies investigating the eff ects of non-pharmacological interventions, such as physical training (PT), on cardiac autonomic control, assessed the HRV only in resting conditions. Recently, a new time-frequency mathematical approach based on the short-time Fourier transform (STFT) method has been validated for the assessment of HRV in non-stationary conditions such as the immediate post-exercise period. The aim of this study was to evaluate the eff ects of the PT on post-exercise cardiac autonomic control using the time-frequency STFT analysis of the HRV. Methods Twenty-one healthy male volunteers participated in this study. The subjects were initially evaluated for their physical exercise/sport practice and allocated to groups of low physical training (LowPT, n = 13) or high physical training (HighPT, n = 8). The post-exercise HRV was assessed by the STFT method, which provides the analysis of dynamic changes in the power of the low- and high-frequency spectral components (LF and HF, respectively) of the HRV during the whole recovery period. Results Greater LF (from the min 5 to 10) and HF (from the min 6 to 10) in the post-exercise period in the HighPT compared to the LowPT group (P < 0.05) was observed. Conclusion These results indicate that exercise training exerts benefi cial eff ects on post-exercise cardiac autonomic control.

Cardiac autonomic responses after resistance exercise in treated hypertensive subjects

The aim of this study was to assess and to compare heart rate variability (HRV) after resistance exercise (RE) in treated hypertensive and normotensive subjects. Nine hypertensive men [HT: 58.0 ± 7.7 years, systolic blood pressure (SBP) = 133.6 ± 6.5 mmHg, diastolic blood pressure (DBP) = 87.3 ± 8.1 mmHg; under antihypertensive treatment] and 11 normotensive men (NT: 57.1 ± 6.0 years, SBP = 127 ± 8.5 mmHg, DBP = 82.7 ± 5.5 mmHg) performed a single session of RE (2 sets of 15–20 repetitions, 50% of 1 RM, 120 s interval between sets/exercise) for the following exercises: leg extension, leg press, leg curl, bench press, seated row, triceps push-down, seated calf flexion, seated arm curl. HRV was assessed at resting and during 10 min of recovery period by calculating time (SDNN, RMSSD, pNN50) and frequency domain (LF, HF, LF/HF) indices. Mean values of HRV indices were reduced in the post-exercise period compared to the resting period (HT: lnHF: 4.7 ± 1.4 vs. 2.4 ± 1.2 ms2; NT: lnHF: 4.8 ± 1.5 vs. 2.2 ± 1.1 ms2, p < 0.01). However, there was no group vs. time interaction in this response (p = 0.8). The results indicate that HRV is equally suppressed after RE in normotensive and hypertensive individuals. These findings suggest that a single session of RE does not bring additional cardiac autonomic stress to treated hypertensive subjects.

Assessment of Autonomic Function by Phase Rectification of RRInterval Histogram Analysis in Chagas Disease

Background In chronic Chagas disease (ChD), impairment of cardiac autonomic function bears prognostic implications. Phase‑rectification of RR-interval series isolates the sympathetic, acceleration phase (AC) and parasympathetic, deceleration phase (DC) influences on cardiac autonomic modulation. Objective This study investigated heart rate variability (HRV) as a function of RR-interval to assess autonomic function in healthy and ChD subjects. Methods Control (n = 20) and ChD (n = 20) groups were studied. All underwent 60-min head-up tilt table test under ECG recording. Histogram of RR-interval series was calculated, with 100 ms class, ranging from 600–1100 ms. In each class, mean RR-intervals (MNN) and root-mean-squared difference (RMSNN) of consecutive normal RR-intervals that suited a particular class were calculated. Average of all RMSNN values in each class was analyzed as function of MNN, in the whole series (RMSNNT), and in AC (RMSNNAC) and DC (RMSNNDC) phases. Slopes of linear regression lines were compared between groups using Student t-test. Correlation coefficients were tested before comparisons. RMSNN was log-transformed. (α < 0.05). Results Correlation coefficient was significant in all regressions (p < 0.05). In the control group, RMSNNT, RMSNNAC, and RMSNNDC significantly increased linearly with MNN (p < 0.05). In ChD, only RMSNNAC showed significant increase as a function of MNN, whereas RMSNNT and RMSNNDC did not. Conclusion HRV increases in proportion with the RR-interval in healthy subjects. This behavior is lost in ChD, particularly in the DC phase, indicating cardiac vagal incompetence.

Time-frequency analysis of microvolt T-wave alternans in chronic Chagas heart disease

Chagas disease is an endemic infectious disease caused by theprotozoa Trypanosoma cruzi , currently affecting 10–12 million subjectsintheworld [1],primarilyrestrictedtotheAmericancontinent.Infectionis generally acquired early in childhood, with mild nonspecific clinicalpresentation, evolving into a dormant asymptomatic course, defined asindeterminatephase.Inatimecourserangingfrom10to30 years,20to30% of contaminated subjects develop cardiac abnormalities character-izedbyenlargementofcardiacchambersandlifethreateningventriculararrhythmia, the so called Chagas heart disease (ChD). A remarkablecharacteristic of ChD is the occurrence of severe ventricular tachyar-rhythmia(VT)orevensuddencardiacdeath(SCD)asafirstsignofheartdisease. Thus, the development of a diagnostic and risk stratificationmethodtodetectthosesubjectsatahigherriskismandatory,includingthose who may eventually benefit of an implantable cardioverter-defibrillator [2].Microvolt T-wave alternans (MTWA) is a promising noninvasivediagnostic tool, considered capable of detecting subjects with eitherischemicornon-ischemicheartdiseaseatahigherriskforVTandSCD.The method is based on the spectral decomposition of the T-waveamplitude time series of consecutive heartbeats. The occurrence ofalternans is confirmed when the spectral peak the 0.5 cycle-per-beatfrequencyexceedsa predefinedthreshold[3]. Sinceits initialproposaland standardization on early nineties [4,5], several studies hasdemonstrated its clinical utility for risk stratification. However, ithasnotbeentestedinChD.Thepurposeofcurrentstudyistodescribea method and its application for assessing MTWA in ChD.High resolution ECG signals were acquired using orthogonalbipolar XYZ Frank leads during 3 min, in supine position in quietand comfortable environment. Medical instrumentation employed,signal acquisition protocol and pre-processing techniques have beendescribed elsewhere [6]. The routine of ECG signal processingcomprises the following phases: i) Detection of maximum absoluteQRS complex peaks; ii) Identification and delimitation of successiveT-waves; iii) Detection of maximal absolute T-wave peaks, iv)Deployment of a T-wave peaks time series; and v) Time-frequencyanalysis of the generated time series.The QRS complex detection algorithm was based on Pan and Tomp-kins method [7] associated to correlation analysis on a convenienttemplate, which appropriately selected heart beats. A beat editingfacility allowed parameters change for improving detection accuracyand ectopic beats rejection by visual inspection.T-wave detection algorithm considered in short, the followingsteps: i) The onset and the offset of selected T-wave template wasmanually defined; ii) Considering the rate-adaptation processed asdescribed by Bazett formula, the routine further determined theboundaries of the T-wave for each subsequent beat, as oriented byboth the preceding RR interval and the T-wave template interval [8](Fig. 1-a); iii) The maximum absolute value of the analyzing T-waveinterval identified the T-wave peak (Fig. 1-b); iv) A time series ofconsecutiveT-wavepeakswasthusdeployed,whereT-wavealternanswould be eventually assessed (Fig. 1-c). An eventual T-wave peakarising from an ectopic beat in the series was replaced by the averageof accepted beats to avoid spurious oscillation.The spectral analysis was carried out on 3-min T-wave peaktime series segment using a moving short-time Fourier transformalgorithm. To build a time-frequency map, the analyzing time serieswas segmented in 128 beats windows (Fig. 1-d). Each window wasdetrended,multipliedbyaHanningwindowtoavoiddiscontinuities,and then submitted to spectral decomposition via FFT. Each spectralestimate was, then, squared and appropriately transformed tocompose a power spectral density function time–frequency map(Fig. 1-e), in which the following classical indexes were analyzedat each time slice (Fig. 1-f): i) Cumulative voltage alternans (P)(defined as the squared root of the difference between the peakspectral amplitude at 0.5 cycle per beat and the average spectralnoise), where PN1.9 μV was abnormal; and ii) Alternans ration (K)(defined as the difference of the spectral amplitude peak at 0.5 cycleper beat and the average spectral noise, divided by the standarddeviation of spectral noise), where KN3 were abnormal. In thetime frequency map, the Power spectrum is continuously assessedon 128 beats window during the 3-min segment. If at any moment,either indexes overtakes the corresponding normality threshold,alternans is defined.The study protocol was approved by National Institute ofCardiology Ethics Committee (National institute of cardiology proto-col #0190/12.02.2008).A clinically stable subject with ChD, with left ventricular ejectionfraction of 35%, spontaneous episodes of nonsustained ventriculartachycardiaandanimplantedventricularpacemaker-cardioverterwasinvited to participate and provided written informed consent.Ventricular pacemaker was set to an initial ventricular rate of90 bpm, gradually and continuously increased up to 110 bpm andthen decreased back to 100 bpm.The MTWA presence was detected in the ECG signal at aheart rate of 100 bpm. Lead Y provided the best results, and thevoltage alternans (P) and alternans ratio (K) values were, res-pectively,1.2 µV and 3.4 units. Theanalyzed time series of consecutiveT-wave peaks (128 points) where MTWA was detected had noectopic beats.

Dynamic coupling between atrio-ventricular duration and RR-interval histogram phase-rectification analysis in chronic Chagas disease

This study investigated dynamic atrio-ventricular duration (AVD) and phase-rectification-driven RRinterval coupling to assess AV conduction facilitation in healthy and chronic Chagas disease (ChD) subjects. All subjects were in sinus rhythm and underwent 60 min head-up tilt table test under ECG recording. ChD group underwent MIBG scintigraphy and confirmed sympathetic denervation. Histogram of RR-interval series was calculated, with 100 ms class, ranging from 600 ms to 1200 ms. For each class, mean of normal RR-intervals (MRR) and mean of the peak-to-peak P-to-R wave interval (MPR), representing AVD, were analyzed in RR-intervals pairs of acceleration (AC) and deceleration (DC) phases, reflecting sympathetic and parasympathetic influences on heart rate, respectively. Regression lines of MPR vs. MRR were computed in the whole series, and in DC and AC phases, and respective slopes calculated (sMPRT, sMPRDC and sMPRAC). Student t-test compared groups. MRR and MPR were larger in ChD group. In healthy subjects, sMPRT, sMPRDC and sMPRAC significantly increased as a function of MRR in all phases. In subjects with Chagas disease, however, PR-interval increases only in DC phase, confirming loss of sympathetic driven RR-interval variation.