Entity

Time filter

Source Type

Chillarón de Cuenca, Spain

Mateo J.,University of Castilla - La Mancha | Sanchez-Morla E.M.,Hospital Universitario Of Guadalajara | Sanchez-Morla E.M.,University of Alcala | Santos J.L.,Clinical Psychiatry Service
Computers and Electrical Engineering | Year: 2015

Advanced medical diagnosing and research requires precise information which can be obtained from measured electrophysiological data, e.g., electroencephalogram (EEG) and electrocardiograph (ECG). However, they are often contaminated with noise and a variety of bioelectric signals called artefacts, e.g., electromyography (EMG). These noise and artefacts which need to be reduced make it difficult to distinguish normal from abnormal physiological activity. Electromagnetic contamination of recorded signals represents a major source of noise in electrophysiology and impairs the use of recordings for research. In this paper we present an effective method for cancelling 50. Hz (or 60. Hz) interference using a radial basis function (RBF) Wiener hybrid filter. One of the main points of this paper is the hybridization of the RBF filter and a Wiener filter to make full use of both merits. The effectiveness and validity of those filters are verified by applying them to ECG and EEG recordings. The results show that the proposed method is able to reduce powerline interference (PLI) from the noisy ECG and EEG signals more accurately and consistently in comparison to some of the state of-the-art methods and this method can be efficiently used with very low signal-to-noise ratios, while preserving original signal waveform. © 2014 Elsevier Ltd.


Torres A.M.,University of Castilla - La Mancha | Mateo J.,University of Castilla - La Mancha | Garcia M.A.,Clinical Neurophysiology Service | Santos J.L.,Clinical Psychiatry Service
Circuits, Systems, and Signal Processing | Year: 2015

Powerline contamination of recorded signals represents a major source of noise in electrophysiology and impairs the use of recordings for research. Furthermore it degrades the signal quality and overwhelms tiny features that may be critical for clinical monitoring and diagnosis. During last years, notch filters and adaptive cancellers have been suggested to suppress this interference. In this article we present an improved adaptive canceller for the reduction of the fundamental powerline interference component and harmonics in electrocardiogram (ECG) and electrocardiograph (EEG) recordings. In this new ECG and EEG denoising approach is used an affine projection (AP) algorithm based on Gauss–Seidel method. The results show that the proposed method is able to reduce powerline interference from the noisy ECG and EEG signals more accurately and consistently in comparison to some of the state of-the-art methods. Furthermore, AP can be efficiently used with very low signal-to-noise ratios, while preserving original signal waveform. © 2014, Springer Science+Business Media New York.


Mateo J.,University of Castilla - La Mancha | Torres A.M.,University of Castilla - La Mancha | Aparicio A.,Clinical Psychiatry Service | Santos J.L.,Clinical Psychiatry Service
Computers and Electrical Engineering | Year: 2015

The analysis of the surface Electrocardiogram (ECG) is the most extended non-invasive technique in cardiological diagnosis. The ectopic beats are heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis. These beats are very common in atrial fibrillation (AF), causing important residua when ventricular activity has to be removed for atrial activity (AA) analysis. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. In this work, a method is proposed to cancel out ectopics by classification between normal and abnormal beats. The systems is based on Radial Basis Function Neural Network (RBFNN). This new approach is compared to state-of-the-art techniques for the ectopic classification and cancellation in the MIT database. The results clearly demonstrated the improved ECG beats classification accuracy compared with other alternatives and a very accurate reduction of ectopic beats together with low distortion of the QRST complex. © 2015.

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