Usefulness of ventricular sense response in last-generation cardiac resynchronization therapy devices.

OBJECTIVES: High percentages of pacing were associated to maximal symptomatic and mortality benefit from cardiac resynchronization therapy (CRT). Loss of CRT pacing is linked to intrinsic ventricular activation preceding biventricular pacing (BiV), as it occurs in patients with atrial fibrillation (AF). Last generation CRT devices incorporate the ventricular sense response (VSR) mechanism to maintain biventricular pacing in patients with atrial arrhythmias. This work aimed to characterize electrical dyssynchrony differences among baseline, BiV and VSR pacing, and determine whether the VSR mode is as beneficial as the BiV mode in terms of electrical dyssynchrony. METHODS: Thirty-two patients implanted with CRT devices were retrospectively studied. All patients presented non-ischemic dilated myocardiopathy and complete left bundle branch block (LBBB). Every patient went through baseline, BiV and VSR pacing while recording the 12­lead ECG. Electrical dyssynchrony was assessed by a dyssynchrony index (DIn) obtained from correlation analysis on the 12­lead ECG. RESULTS: When comparing with baseline, VSR pacing improved QRS duration (178 ± 22 ms vs 158 ± 43 ms, baseline vs VSR, p < 0.05) and so did BiV pacing (178 ± 22 ms vs 142 ± 20 ms, baseline vs BiV, p < 0.05). However, electrical dyssynchrony only improved at BiV pacing (2.86±0.6 vs 0.54±0.8, baseline vs BiV, p < 0.05) while VSR showed average DIn values similar to those at baseline. CONCLUSIONS: VSR pacing did not improve the electrical synchrony while did shorten QRS duration in this sample population. Therefore, VSR paced beats would fall in the category of inefficient BiV and may not be the preferred alternative in patients with CRT and AF.

Modelling of the electrocardiographic signal during an angioplasty procedure in the right coronary artery.

Dynamical models are useful tools to generate sets of varied morphological signals by synthesizing human electrocardiograms (ECGs). These signals are used for testing and improving algorithms of ECG delineation, patient monitoring and heart disease detection. This work presents a procedure based on the ECGSYN model to synthesize ECG morphological changes induced by a percutaneous transluminal coronary angioplasty (PTCA) procedure in the right coronary artery. We provide a set of parameters to be used in ECGSYN and generate heartbeats with altered ST-T complexes. These characteristic model parameters were obtained through a non-linear fitting algorithm applied to every available heartbeat. To extend these parameters, normal distributions were generated with their means and standard deviations obtained from the STAFF III database. Parameters were presented for P, QRS and T-waves at leads II, III and aVF. The synthesis procedure shows an average correlation and positive predictive value of 92.2% and 88.2%, respectively. In conclusion, we provide a technique capable of synthesizing electrocardiographic ischemic morphology with physiological plausibility. Then, the generation of data sets for algorithm testing can benefit from this system of ECG signal synthesis.

ECG parameters to predict left ventricular electrical delay.

AIMS: Left ventricular (LV) dyssynchrony lengthens the left ventricular electrical delay (LVED), measured from QRS onset to the first peak of the LV electrogram. We constructed an ECG model to predict LVED noninvasively. METHODS: Intrapatient LVED was measured during a baseline vs nonselective His bundle pacing (nHBP) protocol. This setup provided paired synchronic/non-synchronic LVEDs, allowing intrapatient comparisons. Crosscorrelation of leads II and V6 was accomplished and extracted features together with age and gender fed a linear mixed effects model to predict LVED. RESULTS: Hemodynamic increments were consistent with LVED advances under nHBP in a subset of 17 patients (dP/dtmax, baseline: 938.82 ± 241.95 mm Hg/s vs nHBP: 1034.94 ± 253.63 mm Hg/s, p = 6.24e-4). The inclusion of the area under V6 (AV 6) and the time shift of R-peaks obtained from the crosscorrelation signal (CorS) grouped by patient significantly improved LVED estimation with respect to the model based only on QRS duration, age and gender (p = 1.7e-5). CONCLUSIONS: Interlead ECG changes explained LVED, providing clues about the electrical impulse conduction within the left ventricle noninvasively.

Non-selective His bundle pacing with a biphasic waveform: enhancing septal resynchronization.

Aims: His bundle pacing has shown to prevent detrimental effects from right ventricular apical pacing (RVA) and proved to resynchronize many conduction disturbances cases. However, the extent of His bundle pacing resynchronization is limited. An optimized stimulation waveform could expand this limit when implemented in His bundle pacing sets. In this work, we temporarily implemented RVA and Non-selective His bundle pacing with a biphasic anodal-first waveform (AF-nHB) and compared their effects against sinus rhythm (SR). Methods and results: Fifteen patients referred for electrophysiologic study with conduction disturbances, cardiomyopathy and ejection fraction below 35% were enrolled for the study. The following acute parameters were measured: QRS duration, left ventricular activation (RLVT), time of isovolumic contraction (IVCT), ejection fraction (EF), and dP/dtmax. QRS duration and RLVT decreased markedly under AF-nHB (SR: 169 ± 34 ms vs. nHB: 116 ± 31 ms, P < 0.0005) while RVA significantly increased QRS duration (SR: 169 ms vs. RVA: 198 ms, P < 0.05) and did not change RLVT (P = NS). Consistently, IVCT moderately decreased under AF-nHB (SR: 238 ms vs. RVA: 184 ms, P < 0.05 vs. SR) and dP/dtmax showed a 93.35 [mmHg] average increase under AF-nHB against SR. Also, T-wave inversions were observed during AF-nHB immediately after SR and RVA pacing suggesting the occurrence of cardiac memory. Conclusions: AF-nHB corrected bundle branch blocks in patients with severe conduction disturbances, even in those with dilated cardiomiopathy, outstanding from RVA. Also, the occurrence of cardiac memory during AF-nHB turned up as an observational finding of this study.

The allometric model in chronic myocardial infarction.

BACKGROUND: An allometric relationship between different electrocardiogram (ECG) parameters and infarcted ventricular mass was assessed in a myocardial infarction (MI) model in New Zealand rabbits. METHODS: A total of fifteen animals were used, out of which ten underwent left anterior descending coronary artery ligation to induce infarction (7-35% area). Myocardial infarction (MI) evolved and stabilized during a three month-period, after which, rabbits were sacrificed and the injured area was histologically confirmed. Right before sacrifice, ECGs were obtained to correlate several of its parameters to the infarcted mass. The latter was normalized after combining data from planimetry measurements and heart weight. The following ECG parameters were studied: RR and PR intervals, P-wave duration (PD), QRS duration (QRSD) and amplitude (QRSA), Q-wave (QA), R-wave (RA) and S-wave (SA) amplitudes, T-wave peak amplitude (TA), the interval from the peak to the end of the T-wave (TPE), ST-segment deviation (STA), QT interval (QT), corrected QT and JT intervals. Corrected QT was analyzed with different correction formulae, i.e., Bazett (QTB), Framingham (QTFRA), Fridericia (QTFRI), Hodge (QTHO) and Matsunaga (QTMA) and compared thereafter. The former variables and infarcted ventricular mass were then fitted to the allometric equation in terms of deviation from normality, in turn derived after ECGs in 5 healthy rabbits. RESULTS: Six variables (JT, QTB, QA, SA, TA and STA) presented statistical differences among leads. QT showed the best allometric fit (r = 0.78), followed by TA (r = 0.77), STA (r = 0.75), QTFRA (r = 0.72), TPE (r = 0.69), QTFRI (r = 0.68) and QTMA (r = 0.68). Corrected QT's (QTFRA, QTFRI and QTMA) performed worse than the uncorrected counterpart (QT), the former scaling allometrically with similar goodness of fits. CONCLUSIONS: QT, TA, STA and TPE could possibly be used to assess infarction extent in an old MI event through the allometric model as a first approach. Moreover, the TPE also produced a good allometric scaling, leading to the potential existence of promising allometric indexes to diagnose malignant arrhythmias.

Probability of ventricular fibrillation: allometric model based on the ST deviation.

BACKGROUND: Allometry, in general biology, measures the relative growth of a part in relation to the whole living organism. Using reported clinical data, we apply this concept for evaluating the probability of ventricular fibrillation based on the electrocardiographic ST-segment deviation values. METHODS: Data collected by previous reports were used to fit an allometric model in order to estimate ventricular fibrillation probability. Patients presenting either with death, myocardial infarction or unstable angina were included to calculate such probability as, VFp = δ + ß (ST), for three different ST deviations. The coefficients δ and ß were obtained as the best fit to the clinical data extended over observational periods of 1, 6, 12 and 48 months from occurrence of the first reported chest pain accompanied by ST deviation. RESULTS: By application of the above equation in log-log representation, the fitting procedure produced the following overall coefficients: Average ß = 0.46, with a maximum = 0.62 and a minimum = 0.42; Average δ = 1.28, with a maximum = 1.79 and a minimum = 0.92. For a 2 mm ST-deviation, the full range of predicted ventricular fibrillation probability extended from about 13% at 1 month up to 86% at 4 years after the original cardiac event. CONCLUSIONS: These results, at least preliminarily, appear acceptable and still call for full clinical test. The model seems promising, especially if other parameters were taken into account, such as blood cardiac enzyme concentrations, ischemic or infarcted epicardial areas or ejection fraction. It is concluded, considering these results and a few references found in the literature, that the allometric model shows good predictive practical value to aid medical decisions.

Prediction of ventricular fibrillation based on the ST-segment deviation: allometric model.

Based on some reported clinical data, we attempt to apply the allometric law for evaluating the probability of ventricular fibrillation when electrocardiographic ST-segment deviations are determined. The deviation is measured in millimeters at the standard calibration of 1mV = 10mm and the probability in percent. Using the equation VF(P) = δ + ß (ST) in log-log representation, the fitting procedure produced the following overall coefficients: Average ß = 1.11, with a maximum = 1.65 and a minimum = 0.78; Average δ = 0.83, with a maximum = 1.39 and a minimum = 0.41. For a 2mm ST-deviation, the full range of predicted ventricular fibrillation probability extended from about 6% at 1 month up to 47% at 4 years after the original cardiac event. These results, at least preliminarily, appear acceptable and still call for full clinical test. The model seems promising if other parameters were taken into account, such as cardiac enzyme concentration, ischemic or infarcted epicardial areas or ejection fraction. It is concluded, considering these results and a few references found in the literature, that the allometric model shows promising features in cardiology.

DATA-MEAns: an open source tool for the classification and management of neural ensemble recordings.

The number of laboratories using techniques that allow to acquire simultaneous recordings of as many units as possible is considerably increasing. However, the development of tools used to analyse this multi-neuronal activity is generally lagging behind the development of the tools used to acquire these data. Moreover, the data exchange between research groups using different multielectrode acquisition systems is hindered by commercial constraints such as exclusive file structures, high priced licenses and hard policies on intellectual rights. This paper presents a free open-source software for the classification and management of neural ensemble data. The main goal is to provide a graphical user interface that links the experimental data to a basic set of routines for analysis, visualization and classification in a consistent framework. To facilitate the adaptation and extension as well as the addition of new routines, tools and algorithms for data analysis, the source code and documentation are freely available.