Analyzing high-density ECG signals using ICA

Yi Zhu*, Amirali Shayan, Wanping Zhang, Tong Lee Chen, Tzyy Ping Jung, Jeng Ren Duann, Scott Makeig, Chung Kuan Cheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The analysis of ECG signals is of fundamental importance for cardiac diagnosis. Conventional ECG recordings, however, use a limited number of channels (12) and each records a mixture of activities generated in different parts of the heart. Therefore, direct observation of the ECG signals collected on the body surface is likely an inefficient way to study and diagnose cardiac abnormalities. This study describes new experimental and analytical methods to capture more meaningful ECG component signals, each representing more directly a physical cardiac source. This study first describes a simply applied method for collecting high-density ECG signals. The recorded signals are then separated by independent component analysis (ICA) to obtain spatially fixed and temporally independent component activations. Results from five subjects show that P-, QRS-, and T-waves can be clearly separated from the recordings, suggesting ICA might be an effective and useful tool for high-density ECG analysis, interpretation, and diagnosis.

Original languageEnglish
Pages (from-to)2528-2537
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume55
Issue number11
DOIs
StatePublished - Nov 2008

Keywords

  • Blind signal separation
  • ECG
  • High-density surface ECG
  • Independent component analysis (ICA)
  • Noninvasive imaging

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