Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings

Abstract

The electrocardiogram (ECG) is the most widely used method for diagnosis of heart diseases, where a good quality of recordings allows the proper interpretation and identification of physiological and pathological phenomena. However, ECG recordings often have interference from noises including thermal, muscle, baseline and powerline noises. These signals severely limit ECG recording utility and, hence, have to be removed. To deal with this problem, the present paper proposes an artificial neural network (ANN) as a filter to remove all kinds of noise in just one step. The method is based on a growing ANN which optimizes both the number of nodes in the hidden layer and the coefficient matrices, which are optimized by means of the Widrow–Hoff delta algorithm. The ANN has been trained with a database comprising all kinds of noise, both from synthesized and real ECG recordings, in order to handle any noise signal present in the ECG. The proposed system improves results yielded by conventional techniques of ECG filtering, such as FIR-based systems, adaptive filtering and wavelet filtering. Therefore, the algorithm could serve as an effective framework to substantially reduce noise in ECG recordings. In addition, the resulting ECG signal distortion is notably more reduced in comparison with conventional methodologies. In summary, the current contribution introduces a new method which is able to suppress all ECG interference signals in only one step with low ECG distortion and a high noise reduction.

Keywords::

• Biomedical signals
• Muscle and baseline noise
• Powerline interference
• Electrocardiogram
• Artificial neural network

Acknowledgements

This work was partly sponsored by Universidad de Castilla-La Mancha, Patronato Universitario Cardenal Gil de Albornoz from Cuenca, the project TEC2010–20633 from the Spanish Ministry of Science and Innovation and PII2C09–0224–5983 and PII1C09–0036–3237 from Junta de Comunidades de Castilla La Mancha.

Declaration of interest: No conflict of interest