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Biosignals are usually contaminated with artifacts from limb movements, muscular contraction or electrical interference. Many algorithms of the literature, such as threshold methods and adaptive filters, focus on detecting these noisy patterns. This study introduces a novel method for noise and artifact detection in electrocardiogram based on time series clustering. The algorithm starts with the extraction of features that best characterize the shape and behaviour of the signal over time and groups its samples in separated clusters by means of an agglomerative clustering approach. The method has been tested in numerous datasets to reveal that it is independent on specific records and globally, the algorithm was able to successfully detect noisy patterns and artifacts with a sensitivity of 88%, a specificity of 92% and an accuracy of 91%, demonstrating a good performance in pattern detection based on morphological clustering. This algorithm can be applied to the detection and sectioning of multiple types of noise for more accurate denoising and adapted for signal classification.

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The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.