Atrial signal extraction in atrial fibrillation ECGs exploiting spatial constraints

International audienceThe accuracy in the extraction of the atrial activity (AA) from electrocardiogram (ECG) signals recorded during atrial fibrillation (AF) episodes plays an important role in the analysis and characterization of atrial arrhhythmias. The present contribution puts forward a new method for AA signal automatic extraction based on a blind source separation (BSS) formulation that exploits spatial information about the AA during the T-Q segments. This prior knowledge is used to optimize the spectral content of the AA signal estimated by BSS on the full ECG recording. The comparative performance of the method is evaluated on real data recorded from AF sufferers. The AA extraction quality of the proposed technique is comparable to that of previous algorithms, but is achieved at a reduced cost and without manual selection of parameters

Time-Series Analysis Using Third-Order Recurrence Plots

Higher-order recurrence plots may enable us to reveal more structure than what is possible with traditional recurrence plots (RPs). While RPs attempt to detect recurrence relations by pair-wise comparison of time-delayed embeddings, given a time series, higher-order RPs may detect recurrences by comparing multiple time-delayed embeddings simultaneously. In this work, we limit ourselves to third-order recurrence plots (TORPs) for time series analysis, as they can still be graphed straightforwardly, and propose future directions

Noninvasive Assessment of Spatio-Temporal Recurrence in Atrial Fibrillation

Propagation of Atrial Activity during atrial fibrillation (AF) is a complex phenomenon characterized by a certain degree of recurrence (periodic repetition). In this study, we investigated the possibility to detect recurrence noninvasively from body surface potential map recordings in patients affected by persistent AF, and localize this recurrence both in time and space. Results showed that clusters of recurrence can be identified from body surface recordings in these patients. Moreover, the number of clusters detected and their location on the top-right of the back of the torso were significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. This suggests that noninvasive quantification of recurrence in persistent AF patients is possible, and may contribute to improve patient stratification

Searching for ring-like structures in the Cosmic Microwave Background

In this research we present a new methodology to search for ring-like structures in the CMB. The particular context of this work is to investigate the presence of possible observational effects associated with Conformal Cyclic Cosmology (CCC), known as Hawking points. Although our results are not conclusive due to the statistical disagreement between the CMB sky map and the simulated sky maps in accordance to $\Lambda CDM$, we are able to retrieve ring-like anomalies from an artificial data at $95 \%$ confidence level. Once this discrepancy has been assessed, our method may be able to provide evidence of the presence or absence of Hawking points in the CMB. Hence, we stress the need to continue the theoretical and experimental research in this direction

Detection of Adulteration in Italian Mozzarella Cheese Using Mitochondrial DNA Templates as Biomarkers

Considering the importance of monitoring adulterations of genuine cheeses in the dairy industry, a polymerase chain reaction–based method was developed to detect bovine- specific mitochondrial DNA sequence in Italian water buffalo Mozzarella cheese. DNA was isolated from cheese matrix and governing liquid by organic extractions and kit purifications. Amplifications of a 134-bp fragment were performed with a bovine–specific set of primers designed on the sequence alignment of bovine and buffalo mitochondrial cytochrome oxidase subunit I. The specificity of the primers was tested using DNA from the blood of two species (water buffalo and bovine), which are present together in adulterated Italian Mozzarella cheese. This method reliably detected a content of 0.5 % of bovin milk, making it suitable for routine fraud monitoring

Hybrid Quantum Singular Spectrum Decomposition for Time Series Analysis

Classical data analysis requires computational efforts that become intractable in the age of Big Data. An essential task in time series analysis is the extraction of physically meaningful information from a noisy time series. One algorithm devised for this very purpose is singular spectrum decomposition (SSD), an adaptive method that allows for the extraction of narrow-banded components from non-stationary and non-linear time series. The main computational bottleneck of this algorithm is the singular value decomposition (SVD). Quantum computing could facilitate a speedup in this domain through superior scaling laws. We propose quantum SSD by assigning the SVD subroutine to a quantum computer. The viability for implementation and performance of this hybrid algorithm on a near term hybrid quantum computer is investigated. In this work we show that by employing randomised SVD, we can impose a qubit limit on one of the circuits to improve scalibility. Using this, we efficiently perform quantum SSD on simulations of local field potentials recorded in brain tissue, as well as GW150914, the first detected gravitational wave event.Comment: 18 pages, 6 figure

Detection of Adulteration in Italian Mozzarella Cheese Using Mitochondrial DNA Templates as Biomarkers

Considering the importance of monitoring adulterations of genuine cheeses in the dairy industry, a polymerase chain reaction–based method was developed to detect bovine- specific mitochondrial DNA sequence in Italian water buffalo Mozzarella cheese. DNA was isolated from cheese matrix and governing liquid by organic extractions and kit purifications. Amplifications of a 134-bp fragment were performed with a bovine–specific set of primers designed on the sequence alignment of bovine and buffalo mitochondrial cytochrome oxidase subunit I. The specificity of the primers was tested using DNA from the blood of two species (water buffalo and bovine), which are present together in adulterated Italian Mozzarella cheese. This method reliably detected a content of 0.5 % of bovin milk, making it suitable for routine fraud monitoring

Incidence, prevalence, and trajectories of repetitive conduction patterns in human atrial fibrillation

AIMS: Repetitive conduction patterns in atrial fibrillation (AF) may reflect anatomical structures harbouring preferential conduction paths and indicate the presence of stationary sources for AF. Recently, we demonstrated a novel technique to detect repetitive patterns in high-density contact mapping of AF. As a first step towards repetitive pattern mapping to guide AF ablation, we determined the incidence, prevalence, and trajectories of repetitive conduction patterns in epicardial contact mapping of paroxysmal and persistent AF patients. METHODS AND RESULTS: A 256-channel mapping array was used to record epicardial left and right AF electrograms in persistent AF (persAF, n = 9) and paroxysmal AF (pAF, n = 11) patients. Intervals containing repetitive conduction patterns were detected using recurrence plots. Activation movies, preferential conduction direction, and average activation sequence were used to characterize and classify conduction patterns. Repetitive patterns were identified in 33/40 recordings. Repetitive patterns were more prevalent in pAF compared with persAF [pAF: median 59%, inter-quartile range (41-72) vs. persAF: 39% (0-51), P < 0.01], larger [pAF: = 1.54 (1.15-1.96) vs. persAF: 1.16 (0.74-1.56) cm2, P < 0.001), and more stable [normalized preferentiality (0-1) pAF: 0.38 (0.25-0.50) vs. persAF: 0.23 (0-0.33), P < 0.01]. Most repetitive patterns were peripheral waves (87%), often with conduction block (69%), while breakthroughs (9%) and re-entries (2%) occurred less frequently. CONCLUSION: High-density epicardial contact mapping in AF patients reveals frequent repetitive conduction patterns. In persistent AF patients, repetitive patterns were less frequent, smaller, and more variable than in paroxysmal AF patients. Future research should elucidate whether these patterns can help in finding AF ablation targets

Improving Prediction of Favourable Outcome After 6 Months in Patients with Severe Traumatic Brain Injury Using Physiological Cerebral Parameters in a Multivariable Logistic Regression Model.

BACKGROUND/OBJECTIVE: Current severe traumatic brain injury (TBI) outcome prediction models calculate the chance of unfavourable outcome after 6 months based on parameters measured at admission. We aimed to improve current models with the addition of continuously measured neuromonitoring data within the first 24 h after intensive care unit neuromonitoring. METHODS: Forty-five severe TBI patients with intracranial pressure/cerebral perfusion pressure monitoring from two teaching hospitals covering the period May 2012 to January 2019 were analysed. Fourteen high-frequency physiological parameters were selected over multiple time periods after the start of neuromonitoring (0-6 h, 0-12 h, 0-18 h, 0-24 h). Besides systemic physiological parameters and extended Corticosteroid Randomisation after Significant Head Injury (CRASH) score, we added estimates of (dynamic) cerebral volume, cerebral compliance and cerebrovascular pressure reactivity indices to the model. A logistic regression model was trained for each time period on selected parameters to predict outcome after 6 months. The parameters were selected using forward feature selection. Each model was validated by leave-one-out cross-validation. RESULTS: A logistic regression model using CRASH as the sole parameter resulted in an area under the curve (AUC) of 0.76. For each time period, an increased AUC was found using up to 5 additional parameters. The highest AUC (0.90) was found for the 0-6 h period using 5 parameters that describe mean arterial blood pressure and physiological cerebral indices. CONCLUSIONS: Current TBI outcome prediction models can be improved by the addition of neuromonitoring bedside parameters measured continuously within the first 24 h after the start of neuromonitoring. As these factors might be modifiable by treatment during the admission, testing in a larger (multicenter) data set is warranted