In this paper, we show techniques to examine the stationaxity and the normality of time series as well as results obtained by applying these techniques to EEG data during sleep stages. Many statistical analyses of the EEG data are based on the assumption that the EEG data axe stationary and normally
Stationarity and normality test for biomedical data
β Scribed by H. Sugimoto; N. Ishii; A. Iwata; N. Suzumura
- Publisher
- Elsevier Science
- Year
- 1977
- Weight
- 532 KB
- Volume
- 7
- Category
- Article
- ISSN
- 0010-468X
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β¦ Synopsis
Biomedical data, such as EEG, EMG and neural impulse sequences, are regarded as the stochastic phenomena of biological systems, and the statistical properties of such time series are often examined. Most of the statistical analysis processed in the frequency and the time domain are based on the assumption that the time series is weakly stationary and normally distributed. Therefore, as the basis of the statistical analysis of the biomedical data, it is necessary to know whether they satisfy the conditions of weak stationarity and normality. However, impulse response and evoked potential biomedical data, are not regarded as the stationary time series. Therefore, other analysis is required. In this paper, the authors present four programs, TEST1, TEST2, TEST3 and TEST4, to examine above conditions. Furthermore, in order to clarify the characteristic of each program, the time series were generated by the computer and examined by the test programs.
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