Search

In the framework of the electrocardiography (ECG) signals, this paper describes an original approach to identify heartbeat morphologies and to detect R-wave events. The proposed approach is based on a 'geometrical matching' rule evaluated using a decision function in a local moving-window procedure. The decision function is a normalized measurement of a similarity criterion comparing the windowed input signal with the reference beat-pattern into a nonlinear-curve space. A polynomial expansion model describes the reference pattern. For the curve space, an algebraic-fitting distance is built according to the canonical equation of the unit circle. The geometrical matching approach operates in two stages, i.e., training and detection ones. In the first stage, a learning-method based on genetic algorithms allows us estimating the decision function from training beat-pattern. In the second stage, a level-detection algorithm evaluates the decision function to establish the threshold of similarity between the reference pattern and the input signal. Finally, the findings for the MIT-BIH Arrhythmia Database present about 98% of sensitivity and 99% of positive predictivity for the R-waves detection, using low-order polynomial models. Index Terms--Decision-making functions, electrocardiography, genetic algorithms, polynomial models.

This paper presents a new, quantitative approach to measuring abnormal intra-QRS signals, using the high-resolution electrocardiogram (HRECG). These signals are conventionally known as QRS 'notches and slurs.' They are measured qualitatively and form the basis for the ECG identification of myocardial infarction. The HRECG is used for detection of ventricular late potentials (LP), which are linked with the presence of a reentry substrate for ventricular tachycardia (VT) after a myocardial infarction. LP's are defined as signals from areas of delayed conduction which outlast the normal QRS period. Our objective is to quantify very low-level abnormal signals that may not outlast the normal QRS period. In this work, abnormal intra-QRS potentials (AIQP) were characterized by removing the predictable, smooth part of the QRS from the original waveform. This was represented as the impulse response of an ARX parametric model, with model order selected empirically from a training data set. AIQP were estimated using the residual of the modeling procedure. Critical AIQP parameters to separate VT and non-VT subjects were obtained using discriminant functions. Results suggest that AIQP indexes are a new predictive index of the HRECG for VT. The concept of abnormal intra-QRS potentials permits the characterization of pathophysiological signals contained wholly within the normal QRS period, but related to arrhythmogenesis. The new method may have other applications, such as detection of myocardial ischemia and improved ECG identification of the site of myocardial infarction, particularly in the absence of Q waves. Index Terms - Autoregressive processes, electrocardiography, modeling.

Abstract: This article introduces a novel concept of abnormal intra-QRS potentials (AIQPs) associated with myocardial ischemia. AIQPs are microvolt-level potentials—subtle alterations in the QRS of the high-resolution electrocardiogram (ECG)—isolated from the unfiltered signal-averaged ECG (SAECG) by a method of mathematical modeling. The aims of the study were (1) to determine the characteristics of potentials in the SAECG related to ischemically altered activation during percutaneous transluminal coronary angiography (PTCA), (2) to determine their relationship with standard 12-lead ECG variables, and (3) to investigate whether AIQPs have a specific pathophysiologic basis in myocardial ischemia. Continuous high-resolution ECG data were acquired from 12 patients before, during, and after PTCA. SAECGs were computed every 60 seconds using an enhanced method of signal averaging. AIQP, ST-segment deviation, and changes in standard ECG QRS duration were measured in each 1-minute SAECG. AIQP amplitudes increased significantly during balloon inflation, compared with the preinflation state. AIQPs exhibited a greater prevalence (12 of 12 patients) than ST-segment deviation changes of more than 100 μV (7 of 12 patients), or measurable changes in standard QRS duration (4 of 12 patients). In patients with significant changes in 12-lead ECG variables during balloon inflation, AIQPs were strongly correlated with both ST-segment and QRS-duration changes. AIQP timing was correlated with the artery occluded, suggesting a specific, ischemia-influenced origin of the signal. AIQPs show promise as a time-localized, sensitive new ECG marker of ischemically altered ventricular activation. [Copyright &y& Elsevier]

This paper proposes the integration and analysis of a mathematical model representing the cardiovascular system and its short-term autonomic response to head-up tilt (HUT) testing. A Latin Hypercube Sampling method was applied to design an optimal experimental space, including 19 model parameters coming from the cardiovascular and baroreflex control systems. Then, a global, variance-based sensitivity analysis was applied to quantity the effects of these parameters on heart rate and systolic blood pressure. Results highlight the relevant influence of the intrinsic heart rate and the sympathetic and parasympathetic baroreflex gains on heart rate regulation, as well as the impact of left ventricle diastolic parameters on systolic blood pressure. Moreover, a significant effect of right ventricle dynamics on blood pressure was noted. These results provide valuable information for the application of such an integrated model for the analysis of the autonomic mechanisms regulating the cardiovascular response induced by postural changes. In particular, they suggest a convenient set of parameters to be identified in a subject-specific manner.

This paper describes the integration of mathematical models of the cardiac electrical activity, the cardiovascular system and the baroreceptor reflex control of the autonomic nervous system, with a model representing a head-up tilt test. Sensitivity analyses are performed in order to determine those model parameters producing the greatest significant effects on heart rate and blood pressure. An optimization step is then applied to the most influential parameters in order to find the best model fit to real cardiac data obtained from a patient suffering from Brugada syndrome and a healthy subject, in supine and upright postures during a tilt test. Results show the feasibility of the model to generate realistic autonomic responses to head-up tilt testing in health and disease.

The purpose of this study was to describe the changes in high-frequency QRS (HF-QRS) components due to myocardial ischemia provoked by prolonged artery occlusion during percutaneous coronary interventions (PCI). Signal-averaged ECGs from 69 patients were obtained during PCI procedures and comparison of high-frequency components of the QRS at different temporal regions and frequency bandwidth were performed. Continuous wavelet transform was applied to estimate the energy contents over the studied time-frequency regions. Seven frequency bands from 50 to 300 Hz, with bandwidth = 100 Hz were considered. The sum of all 12 leads energy decreased significantly (p < 0.001) from pre-PCI to PCI during both second half and total QRS complex in all frequency bands, but the main effect was found in the 200-300 Hz band. The energy changes were more marked toward higher frequency bands. The second half of QRS complex was more sensitive to changes due to myocardial ischemia.

We assess the autonomic nervous system response during prolonged percutaneous transluminal coronary angioplasty (PTCA) by heart rate variability analysis using multifractal indices. These indices are also used to evaluate the effects of the PTCA at different arteries. The indices augmented significantly during transient ischemia and reperfusion periods indicating an increase of multifractal degree and a decrease of the long-range dependence on heartbeat fluctuations. This indicates that significant multifractal complex reactions of autonomic control of the heart rate occurred during coronary artery occlusions. Key words: multifractal analysis, heartbeat fluctuations, myocardial ischemia, coronary artery occlusion.

Coronary artery occlusions related to myocardial ischemia drive cardiac control system reactions that may lead to heart failure. The purpose of this study was to assess the autonomic nervous system (ANS) response during prolonged percutaneous transluminal coronary angioplasty (PTCA). Continuous ECG data were acquired from 50 patients before and during PTCA, with occlusions in the left anterior descending, left circumflex or right coronary artery. Heart rate variability (HRV) was analyzed for 3-min segments of the R-R interval signal obtained from ECG data. The ANS behavior was evaluated by HRV analysis using fractal-like indices. The fractal scalar exponent alpha(1) and power-law slope beta decreased considerably during PTCA. This indicates that significant reactions of autonomic control of the heart rate occurred during coronary artery occlusions, with a reduction in complexity of the ANS.