A. Hernandez

Parameter optimization of awavelet-based electrocardiogram delineator with an evolutionary algorithm

A recurrent problem encountered in many algorithms proposed to detect and segment ECG waves is the adjustment of the numerous parameters used. This work presents a method to optimize these parameters with an evolutionary algorithm (EA). The signal processing chain contains a filter to remove baseline wandering, a QRS detector (Pan & Tompkins) and a wave segmentation step based on the wavelet-transform (WT). The EA adjusts the parameters of the segmentation step in order to minimize the result of a cost function which measures how close the detector is from characteristic points annotated by a cardiologist. Results obtained with the QTDB are compared with other approaches of wave segmentation for which thresholds have been experimentally defined. EAs have shown to be an effective method to solve this complex problem of multiobjective optimization

Study of spectral components of ventricular repolarization variability by multiple correspondence analysis

In this study we propose a novel approach, based on multiple correspondences analysis (MCA), to identify the relationships between the spectral variables obtained from HRV signals and those from ventricular repolarization variability (VRV). Parameters were extracted from RR and RT time series from 47 ECG records from aerobic and anaerobic sportsmen and sedentary subject. These numerical variables were transformed in categorical variables using a 7 classes fuzzy coding and several MCA were carried out. Results show that: i) aerobic disciplines present more variability than anaerobic ones, ii) mean RT interval are longer in sportsmen than in control subjects, iii) only the low frequencies of the HRV and VRV series are correlated, evidencing the heart rate modulation on ventricular repolarization, iv) the high frequencies of the RT space seem to be inadequate markers for the characterization of the ANS modulation on ventricular repolarization

Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night

OBJECTIVE Ventricular arrhythmias in Brugada syndrome (BS) mainly occur at rest, especially during nighttime, suggesting that parasympathetic activity at night may play an important role in the arrhythmogenesis of the disease. This study examined and compared the autonomic function of symptomatic and asymptomatic BS patients overnight. APPROACH We analyzed various heart rate variability (HRV) and heart rate complexity (HRC) markers in a clinical series including 87 BS patients, where 23 were symptomatic. MAIN RESULTS Statistically significant differences were found in markers MIRR, SDNN, SDANN, [Formula: see text] and SampEn, suggesting that symptomatic patients may be related to lower heart rate variability and complexity values, as well as to greater circadian fluctuations overnight. SIGNIFICANCE The results provide further evidence for the role of autonomic imbalance in the pathophysiology of BS, highlighting the relevance of nighttime analysis to the unmasking of significant ANS changes. Based on these outcomes, the role of HRV and HRC assessment at night could be a step forward towards the understanding of BS and the risk for the occurrence of symptoms in these patients, with a potential future impact on therapeutic strategies.