Noise detection on ECG based on agglomerative clustering of morphological features

João Rodrigues, David Belo, Hugo Gamboa

Research output: Contribution to journalArticle

25 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)322-334
Number of pages13
JournalComputers in Biology and Medicine
Publication statusPublished - 1 Aug 2017


  • Agglomerative clustering
  • Artifacts
  • ECG
  • Electrocardiogram
  • Features
  • Morphology
  • Noise detection
  • Pattern recognition
  • Shape
  • Time series

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