Jérôme Van Zaen

Adaptive tracking of EEG oscillations

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.

Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation

AIMS This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG). METHODS AND RESULTS Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N= 20) or long-standing AF (N= 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9% were obtained, with a predictive value for long-standing AF of 93.1%. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG. CONCLUSION Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings.

Alterations in postural control during the world's most challenging mountain ultra-marathon.

We investigated postural control (PC) effects of a mountain ultra-marathon (MUM): a 330-km trail run with 24000 m of positive and negative change in elevation. PC was assessed prior to (PRE), during (MID) and after (POST) the MUM in experienced ultra-marathon runners (n = 18; finish time = 126±16 h) and in a control group (n = 8) with a similar level of sleep deprivation. Subjects were instructed to stand upright on a posturographic platform over a period of 51.2 seconds using a double-leg stance under two test conditions: eyes open (EO) and eyes closed (EC). Traditional measures of postural stability (center of pressure trajectory analysis) and stabilogram-diffusion analysis (SDA) parameters were analysed. For the SDA, a significantly greater short-term effective diffusion was found at POST compared with PRE in the medio-lateral (ML; Dxs) and antero-posterior (AP) directions (Dys) in runners (p<0.05) The critical time interval (Ctx) in the ML direction was significantly higher at MID (p<0.001) and POST (p<0.05) than at PRE in runners. At MID (p<0.001) and POST (p<0.05), there was a significant difference between the two groups. The critical displacement (Cdx) in the ML was significantly higher at MID and at POST (p<0.001) compared with PRE for runners. A significant difference in Cdx was observed between groups in EO at MID (p<0.05) and POST (p<0.005) in the ML direction and in EC at POST in the ML and AP directions (p<0.05). Our findings revealed significant effects of fatigue on PC in runners, including, a significant increase in Ctx (critical time in ML plan) in EO and EC conditions. Thus, runners take longer to stabilise their body at POST than at MID. It is likely that the mountainous characteristics of MUM (unstable ground, primarily uphill/downhill running, and altitude) increase this fatigue, leading to difficulty in maintaining balance.

Systematic comparison of non-invasive measures for the assessment of atrial fibrillation complexity: a step forward towards standardization of atrial fibrillation electrogram analysis

AIMS To present a comparison of electrocardiogram-based non-invasive measures of atrial fibrillation (AF) substrate complexity computed on invasive animal recordings to discriminate between short-term and long-term AF. The final objective is the selection of an optimal sub-set of measures for AF complexity assessment. METHODS AND RESULTS High-density epicardial direct contact mapping recordings (234 leads) were acquired from the right and the left atria of 17 goats in which AF was induced for 3 weeks (short-term AF group, N = 10) and 6 months (long-term AF group, N = 7). Several non-invasive measures of AF organization proposed in the literature in the last decade were investigated to assess their power in discriminating between the short-term and long-term group. The best performing measures were identified, which when combined attained a correct classification rate of 100%. Their ability to predict standard invasive AF complexity measures was also tested, showing an average R(2) of 0.73 ± 0.04. CONCLUSION An optimal set of measures of the AF substrate complexity was identified out of the set of non-invasive measures analysed in this study. These measures may contribute to improve patient-tailored diagnosis and therapy of sustained AF.

Adaptive Filtering Methods for Identifying Cross-Frequency Couplings in Human EEG

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.