Unsupervised Common Spatial Patterns

The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurto sis maximization performs CSP in an unsupervised manner, i.e., with no need for labeled data, when the classes follow Gaussian or elliptically symmetric distributions. Numerical analyses on synthetic and real data validate these findings in various experimental conditions, and demonstrate the interest of the proposed unsupervised approach.Ministerio de Economía y Competitividad (España) TEC2017-82807-

L1-norm unsupervised Fukunaga-Koontz transform

Article number 107942The Fukunaga-Koontz transform (FKT) is a powerful supervised feature extraction method used in twoclass recognition problems, particularly when the classes have equal mean vectors but different covariance matrices. The present work proves that it is also possible to perform the FKT in an unsupervised manner, sparing the need for labeled data, by using a variant of L1-norm Principal Component Analysis (L1-PCA) that minimizes the L1-norm in the feature space. Rigorous proof is given in the case of data drawn from a mixture of Gaussians. A working iterative algorithm based on gradient-descent in the Stiefel manifold is put forward to perform L1-norm minimization with orthogonal constraints. A number of numerical experiments on synthetic and real data confirm the theoretical findings and the good convergence characteristics of the proposed algorithm

Unsupervised and Computationally Lightweight Spectrum Sensing in IoT Devices †

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Principal component analysis (PCA) is a widespread technique in data analysis. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA due to its greater robustness to outliers. The present work shows that, with a whitening step, L1-PCA can perform spectrum sensing and modulation recognition in IoT applications. Numerical experiments confirm this finding

Atrial Signal Extraction in Atrial Fibrillation Electrocardiograms Using a Tensor Decomposition Approach

International audienceAtrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice and remains a major challenge in cardiology. The noninvasive analysis of AF usually requires the estimation of the atrial activity (AA) signal in surface electrocardiogram (ECG) recordings. The present contribution puts forward a tensor decomposition approach for noninvasive AA extraction in AF ECG recordings. As opposed to the matrix approach, tensor decompositions are generally unique under mild conditions and have the potential to perform source separation in scenarios with a limited number of electrodes. An experimental study on a synthethic signal model and a real AF ECG recording evaluates the performance of the so-called block term tensor decomposition approach as compared to matrix techniques such as principal component analysis and independent component analysis

Noninvasive Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation by Fibrillatory Wave Amplitude Computation in Multiple ECG Leads

International audienceBackground. Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging and reported results are perfectible. Improving patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation for patients with low odds of success. CA outcome can be predicted noninvasively by atrial fibrillatory wave (f-wave) amplitude, but previous works have mostly focused on manual measures in single ECG leads only.Aims. The present work aims at assessing the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads.Methods and Results. Sixty-two persistent AF patients (52 males, 61.5±10.4 years) referred to CA were enrolled in this study. During an average follow-up of 14±8 months, 47 patients had no AF recurrence after ablation. A standard one-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a noninvasive signal processing algorithm and combined into a multivariate prediction model based on logistic regression. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an AUC of 0.854 and improving on single-lead amplitude-based predictors.Conclusion. Analyzing the f-wave amplitude simultaneously in several ECG leads can significantly improve CA long-term outcome prediction in persistent AF over predictors based on single-lead measures

Predicting Catheter Ablation Outcome in Persistent Atrial Fibrillation via Multivariate Analyses of ECG Fibrillatory Wave Amplitude

International audienc

Spectral and spatiotemporal variability ECG parameters linked to catheter ablation outcome in persistent atrial fibrillation

International audienceWith the increasing prevalence of atrial fibrillation (AF), there is a strong clinical interest in determining whether a patient suffering from persistent AF will benefit from catheter ablation (CA) therapy at long term. This work presents several regression models based on noninvasive measures automatically computed from the standard 12- lead electrocardiogram (ECG) such as AF dominant frequency (DF), spectral concentration and spatiotemporal variability (STV). Sixty-two AF patients referred to CA were enrolled in this study. Forty-seven of them had no recurrence after CA during an average follow-up of 14 ± 8 months. The ECG features were extracted from an ECG recorded before the CA intervention and they were combined by means of logistic regression. The combination of DF and STV values from different precordial leads reached AUC = 0.939, outperforming the best results by using only one kind of features, such as DF (AUC = 0.801), and yielding a global accuracy of 93.5% for discriminating the best long-term responders to CA. These results point out the need to take into consideration the spatial variation of spectral ECG parameters to build predictive models dealing with AF

A Tensor Decomposition Approach to Noninvasive Atrial Activity Extraction in Atrial Fibrillation ECG

International audienceAtrial fibrillation (AF), the most common arrhythmia in adults, is still considered as the last great frontier of cardiac electrophysiology, since its mechanisms are not completely understood. Analysis of the atrial activity (AA) signal contained in electrocardiograms during AF episodes is a noninvasive and inexpensive solution for obtaining useful information about AF. This work presents tensor decompo-sitions as an alternative to classic blind source separation methods based on matrix decompositions due to their appealing uniqueness properties and considers in particular the block term decomposition (BTD). The practical usefulness of BTD is evaluated by comparing its AA estimation quality, measured by spectral concentration, to those of two benchmark methods, revealing that BTD presents a better performance. The results presented in this work motivate further investigation of tensor decompositions for AF analysis