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In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications, namely ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection. Rel-En successfully identified the events in these settings. Compared to the state-of-the-art, better or comparable results were obtained on QRS-complex and K-complex detection. For iPPG peak detection, the proposed method was used as a preprocessing step to a fixed threshold algorithm that lead to a significant improvement in overall results. While easily defined and computed, Rel-En robustly extracted short-term events of interest. The proposed algorithm can be implemented by two filters and its parameters can be selected easily and intuitively. Furthermore, Rel-En algorithm can be used in other biomedical signal processing applications where a need of short-term event extraction is present. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Due to their transient nature, spontaneous terminations of atrial fibrillation (AF) are difficult to investigate. Apparently, confounding experimental findings about the time scale of this phenomenon have been reported, with values ranging from 1 s to 1 min. We propose a biophysical modeling approach to study the mechanisms of spontaneous termination in two models of AF with different levels of dynamical complexity. 8 s preceding spontaneous terminations were studied and the evolution of cycle length and wavefront propagation were documented to assess the time scale and anatomical location of the phenomenon. Results suggest that termination mechanisms are dependent on the underlying complexity of AF. During simulated AF of low complexity, the total process of spontaneous termination lasted 3,200 ms and was triggered in the left atrium 800 ms earlier than in the right atrium. The last fibrillatory activity was observed more often in the right atrium. These asymmetric termination mechanisms in both time and space were not observed during spontaneous terminations of complex AF simulations, which showed less predictable termination patterns lasting only 1,600 ms. This study contributes to the interpretation of previous clinical observations, and illustrates how computer modeling provides a complementary approach to study the mechanisms of cardiac arrhythmias. [ABSTRACT FROM AUTHOR]

SummaryHigh-intensity transient signals (HITS) detected by transcranial Doppler (TCD) ultrasound may correspond to artifacts or to microembolic signals, the latter being either solid or gaseous emboli. The goal of this study was to assess what can be achieved with an automatic signal processing system for artifact/microembolic signals and solid/gas differentiation in different clinical situations. The authors studied 3,428 HITS in vivo in a multicenter study, i.e., 1,608 artifacts in healthy subjects, 649 solid emboli in stroke patients with a carotid stenosis, and 1,171 gaseous emboli in stroke patients with patent foramen ovale. They worked with the dual-gate TCD combined to three types of statistical classifiers: binary decision trees (BDT), artificial neural networks (ANN), and support vector machines (SVM). The sensitivity and specificity to separate artifacts from microembolic signals by BDT reached was 94% and 97%, respectively. For the discrimination between solid and gaseous emboli, the classifier achieved a sensitivity and specificity of 81% and 81% for BDT, 84% and 84% for ANN, and 86% and 86% for SVM, respectively. The current results for artifact elimination and solid/gas differentiation are already useful to extract data for future prospective clinical studies.Journal of Cerebral Blood Flow & Metabolism (2004) 24, 860–868; doi:10.1097/01.WCB.0000126235.54306.FA [ABSTRACT FROM AUTHOR]