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Extracting Fundamental Periods to Segment Biomedical Signals.

Anastasia Motrenko, Vadim Strijov

    IEEE Journal of Biomedical and Health Informatics
    |August 16, 2015
    PubMed
    Summary

    This study introduces a new method for segmenting nearly periodic time series, like human gait data. The technique automatically identifies and extracts distinct periodic segments for better analysis and activity recognition.

    Area of Science:

    • Signal Processing
    • Time Series Analysis
    • Biomedical Engineering

    Background:

    • Nearly periodic time series are common in various scientific fields, including human activity recognition.
    • Accurate segmentation of these series into meaningful periods is crucial for data interpretation and analysis.
    • Existing methods for period extraction often lack generality or are application-specific.

    Purpose of the Study:

    • To develop a generalizable method for segmenting nearly periodic time series into period-like segments.
    • To introduce a novel definition of nearly periodic time series using shape and time transformations.
    • To enable automatic extraction of fundamental periodicity for improved time series analysis.

    Main Methods:

    • Defined nearly periodic time series using triplets: basic shape, shape transformation, and time scaling.

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  • Employed Hankel matrix principal component analysis to identify periodic components.
  • Segmented time series by cutting the principal component trajectory at its symmetry axis and merging half-periods.

    • Main Results:

    • Successfully applied the method to segment accelerometric time series of human gait.
    • Demonstrated automatic selection of principal component pairs corresponding to fundamental periodicity.
    • Achieved comparable or superior performance against classical period extraction methods.

    Conclusions:

    • The proposed method offers a general approach for segmenting nearly periodic time series beyond specific applications.
    • Automatic segmentation into periods provides interpretable segments, valuable for human activity recognition.
    • The method is robust and effective for extracting fundamental periodicity from complex time series data.