Extraction of Stationary Spectral Components Using Stochastic Variability
Biosignal recordings are widely used in the medical environment to support the evaluation and the diagnosis of pathologies. Nevertheless, the main difficulty lies in the non-stationary behavior of the biosignals, making difficult the obtention of patterns characterizing the changes in physiological or pathological states. Thus, the obtention of the stationary and non-stationary components of a biosignal poses still an open issue. This work proposes a methodology to detect time-homogeneities based on time-frequency analysis aiming to extract the non-stationary behavior of the biosignal. Two homogeneity constraints are introduced as the measure of stochastic variability of the considered dataset. The first one is the relevance value, which gives information about the contribution of the spectral component to the process. The second one is based on the first and second moments of stochastic variability map, being related to the uniformity along the time of each spectral component. Results show an increase in the stationarity of the reconstructions from the enhanced time-frequency representations. Moreover, the inter class distance for the reconstructed time-series show more discrimination on the stationary component than on the non-stationary one. Those extracted components tend to meet the requirement of most algorithms proposed for other tasks, such as biosignal classification problems, leading to a performance increase of the methodologies.
KeywordsTime-evolving Latent Variable Decomposition Multivariate Locally Stationary Time Series Stationarity Enhancement
- 7.Von Buenau, P., Meinecke, F., Kiraly, F., Mueller, K.: Finding stationary subspaces in multivariate time series. Physical Review Letters 103(21), 214101-1–214101-4 (2009)Google Scholar
- 8.Weng, X., Shen, J.: Finding discordant subsequence in multivariate time series. In: 2007 IEEE International Conference on Automation and Logistics, pp. 1731–1735 (August 2007)Google Scholar