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A biomedical signal segmentation algorithm for event detection based on slope tracing.

Jungkuk Kim1, Minkyu Kim, Injae Won

  • 1Electronic Engineering Department, Myongji University, Yongin, 449-728 Korea. jk.kim@mju.ac.kr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
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A novel signal segmentation algorithm identifies signal components using characteristics like amplitude and slope. Applied to ECG and electrograms, it efficiently segments epochs without complex preprocessing.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computational Biology

Background:

  • Accurate signal segmentation is crucial for analyzing physiological data.
  • Existing methods often require extensive preprocessing and computational complexity.
  • Identifying specific signal components in complex waveforms remains a challenge.

Purpose of the Study:

  • To introduce a simple and efficient signal segmentation algorithm.
  • To demonstrate the algorithm's applicability to electrocardiogram (ECG) and electrogram signals.
  • To highlight the algorithm's ability to segment signal epochs without significant preprocessing.

Main Methods:

  • The algorithm determines signal component epochs based on amplitude, slope, deflection width, and inter-deflection distance.

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  • Epochs are segmented indirectly using a slope trace wave with average slope and a predetermined delay.
  • The method was applied to real-world ECG and electrogram data.

    • Main Results:

    • The algorithm successfully segmented signal epochs in both ECG and electrogram data.
    • It demonstrated practical applicability and high efficiency.
    • The method effectively selected particular signal components without substantial preprocessing.

    Conclusions:

    • The proposed signal segmentation algorithm is simple, efficient, and broadly applicable.
    • It offers a valuable tool for analyzing physiological signals like ECG and electrograms.
    • The algorithm's minimal preprocessing requirement makes it a practical solution for various applications.