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Sparse component analysis techniques have been successfully applied to the separation of speech sources. This paper presents an efficient algorithm based on the matching pursuit approach to deal with multichannel records. The proposed algorithm explicitly employs spatial constraints among different channels to express mixed signals as linear combinations of delayed components selected from an overcomplete dictionary. We present a new procedure for estimating the mixing system parameters (attenuations and delays), which can be applied to more than two mixtures and is not restricted to non-negative attenuation coefficients. The proposed mixing system estimation method can accommodate delays of greater magnitude than traditional approaches. In addition, learned dictionaries that improve the identification step can be used when excerpts from sources (exogenous to mixtures) are available. The simulation results show that semi-blind dictionaries perform better than those used in blind configurations. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is relevant for several medical purposes. In this work, a novel analysis-by-synthesis method to process ECG signals is presented. It is based on the matching pursuit algorithm, which is employed here to decompose the ECG in the time domain. The main features of the ECG are extracted through a dictionary of triangular functions, due to their good correlation with the typical electrocardiographic waveforms, especially the R wave. The individual elements of this signal representation can be further employed for different processing tasks, such as ECG compression and QRS detection. Compression is required to store and transmit signals in situations related to massive acquisitions, frequent monitoring, high-resolution data, real-time needs or narrow bandwidths. QRS detection is not only essential to study the heart rate variability, but also the basis of automatic systems for ECG applications such as heartbeat classification or anomaly identification. In this study, it is shown how to employ the proposed processing approach to perform ECG compression and beat detection jointly. The resulting algorithm is tested over the whole MIT-BIH Arrhythmia Database, with a wide variety of ECG records, yielding both high compression and efficient QRS detection. [ABSTRACT FROM AUTHOR]

When a wireless sensor network (WSN) is employed to monitor environmental data, efficient energy usage is demanded so that the WSN lifetime can be extended. In this context, we propose a distributed energy conservation algorithm (DECA). DECA considers the known past to make predictions on the measurement change over time in order to put sensor nodes into sleep mode, saving energy. Sensor nodes compute their inactivity periods taking into account the inactivity periods of their neighboring nodes. The signals are reconstructed at the sink node, from the sampled values received from sensor nodes. DECA is designed to keep the reconstruction error of the monitored process below a given fraction of its actual value. Since DECA works on the application layer, it can be combined with medium access control and routing protocols to further improve energy management. The proposed algorithm is evaluated with simulations using real data. Results show gains in the network lifetime of up to 1000 % as compared to a WSN without any energy management, while maintaining the error of the reconstructed process at the sink within the desired fraction imposed. Since a strategy for managing the sleeping period in routing nodes is proposed, a metric for evaluating the effects of sleeping periods on network connectivity is also proposed. We have verified that the network connectivity is not impaired by the proposed algorithm, as packet delivery ratios above 90 % were obtained. [ABSTRACT FROM AUTHOR]