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Stationarity and normality test for biomedical data.

H Sugimoto, N Ishii, A Iwata

    Computer Programs in Biomedicine
    |December 1, 1977
    PubMed
    Summary
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    This study introduces four programs to test if biomedical time series data, like EEG and EMG, are stationary and normally distributed. These tests are crucial because standard statistical analysis assumes these properties, which are often not met by biomedical data.

    Area of Science:

    • Biomedical data analysis
    • Time series analysis
    • Statistical signal processing

    Background:

    • Biomedical data (EEG, EMG, neural impulses) are stochastic time series.
    • Standard statistical analysis assumes weak stationarity and normal distribution.
    • Biomedical data, particularly impulse responses and evoked potentials, often violate these assumptions.

    Purpose of the Study:

    • To develop and present computational tools for assessing statistical properties of biomedical time series.
    • To evaluate the suitability of standard statistical methods for biomedical data analysis.
    • To provide methods for identifying non-stationary and non-normally distributed biomedical signals.

    Main Methods:

    • Development of four distinct programs: TEST1, TEST2, TEST3, and TEST4.

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  • Generation of synthetic time series data to characterize program performance.
  • Application of the developed programs to examine stationarity and normality conditions.

    • Main Results:

    • The study presents four programs designed to test for weak stationarity and normal distribution in time series.
    • Computer-generated time series were used to validate the characteristics and performance of each program.
    • The findings highlight the necessity of pre-analysis checks for biomedical data properties.

    Conclusions:

    • Standard statistical assumptions of stationarity and normality are frequently unmet in biomedical time series.
    • The presented programs (TEST1-TEST4) offer a means to assess these critical data characteristics.
    • Accurate statistical analysis of biomedical signals requires verification of underlying assumptions.