Catheter ablation (CA) is a commonly used treatment for persistent atrial fibrillation (AF). Since its medium/long-term success rate remains limited, preoperative prediction of its outcome is gaining clinical interest to optimally select candidates for the procedure. Among predictors based on the surface electrocardiogram, the dominant frequency (DF) and harmonic exponential decay (γ) of the fibrillatory waves (f-waves) have reported promising but clinically insufficient results. Hence, the main goal of this work was to conduct a broader analysis of the f-wave harmonic spectral structure to improve CA outcome prediction through several entropy-based measures computed on different frequency bands. On a database of 151 persistent AF patients under radio-frequency CA and a follow-up of 9 months, the newly introduced parameters discriminated between patients who relapsed to AF and those who maintained SR at about 70%, which was statistically superior to the DF and approximately similar to γ. They also provided complementary information to γ through different combinations in multivariate models based on lineal discriminant analysis and report classification performance improvement of about 5%. These results suggest that the presence of larger harmonics and a proportionally smaller DF peak is associated with a decreased probability of AF recurrence after CA.

Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20–30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3–9% and other previously proposed predictors of ECV by 15–30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.

The origin of the human alpha rhythm has been a matter of debate since Lord Adrian attributed it to synchronous neural populations in the occipital cortex. Although some authors have pointed out the Gaussian characteristics of the alpha rhythm, their results have been repeatedly disregarded in favor of Adrian's interpretation; even though the first EEG Gaussianity reports can be traced back to the origins of the field.Here we revisit this problem using the envelope analysis -a method that relies on the fact that the coefficient of variation of the envelope (CVE) for continuous-time zero-mean Gaussian noise (as well as for any filtered sub-band) is equal to (4 − π)/π ≈ 0.523, thus making the CVE a fingerprint for Gaussianity. As a consequence, any significant deviation from (4 − π)/π is linked to synchronous neural dynamics. We analyzed occipital EEG and iEEG data from massive public databases. Our results showed the human alpha rhythm can be characterized either as a synchronous or as a Gaussian signal based on the value of its CVE.Furthermore, Fourier analysis showed the canonical spectral peak at ≈ 10[Hz] is present in both the synchronous and Gaussian cases, thus demonstrating this same peak can be produced by different underlying neural dynamics. This study confirms the original interpretation of Adrian regarding the origin of the alpha rhythm but also opens the door for the study of Gaussianity in brain dynamics. These results suggest a broader interpretation for event-related synchronization/desynchronization (ERS/ERD) may be needed. Envelope analysis constitutes a novel complement to Fourier-based methods for neural signal analysis relating amplitude modulation patterns (CVE) to signal energy.

Here we introduce a new approach for Gaussianity testing using the envelope of a signal. The coefficient of variation ( std mean ) of the envelope (CVE) of zero-mean Gaussian noise is a universal constant equal to m = (4 − π)/π ≈ 0.523. Thus, for any signal the result CVE = m can be used as a fingerprint to assess Gaussianity. Interestingly, the CVE is also unique for uniform noise time series or low density Filtered Poisson Processes. Here we summarize the mathematics and computer methods behind using the CVE for Gaussianity testing in time series. In particular, we describe how to perform Gaussianity testing in a step-by-step fashion for experimental data using the Hilbert transform, showing that the sampling rate as well as the duration and the filtering of the data stream affect the analysis. Additionally, through the use of the Fourier transform phase randomization, we reveal the interconnections among CVE, Gaussianity and temporal modulation profiles. Furthermore, we use the CVE to assess the degree of synchronization in Kuramoto and Matthews-Mirollo-Strogatz models and show that CVE is relevant to the study of coupled oscillators systems. CVE Gaussianity testing provides a new tool for signal classification.

International audiencePrivacy impacts of video surveillance systems are a major concern. This paper presents our ongoing multidisciplinary approach to integrate privacy concerns in the design of video surveillance systems. The project aims at establishing a reference framework for the collection of privacy concepts and principles, the description of surveillance contexts, surveillance technologies, and accountability capabilities