Wearable sensors are increasingly taking part in daily activities, not only because of the
recent society health concern, but also due to their relevance in the medical industry. In this …

Standard recordings of electrocardiograhic signals are contaminated by a large variety of
noises and interferences, which impair their analysis and the further related diagnosis. In …

The processing and analysis of multichannel signals which exhibit some synchronicity is of
critical importance in biomedical signal processing. Though sparse reconstruction of such …

The electrocardiogram (ECG) was the first biomedical signal for which digital signal
processing techniques were extensively applied. By its own nature, the ECG is typically a …

The use of compressive sensing techniques has become predominant in signal processing
applications when dealing with sparse signals. Theoretical results on these tools involve …

The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation
(AF) conditions is considered in this paper. A mathematical model for the observed signals …

The reconstruction algorithm is one of the core issues in applying compressed sensing
theory to practical applications. Two‐dimensional orthogonal matching pursuit (2DOMP) …

In this paper, a new method is presented to ensure automatic synchronization of intracardiac
ECG data, yielding a three-stage algorithm. We first compute a robust estimate of the …

The electrocardiogram (ECG) was the first biomedical signal where digital signal processing
techniques were extensively applied. By its own nature, the ECG is typically a sparse signal …

In this paper, we describe an efficient iterative algorithm for finding sparse solutions to a
linear system. Apart from the well-known L 1 norm regularization, we introduce an additional …