Time-Frequency Based Feature Extraction for Non-Stationary Signal Classification

Biosignal recordings are useful for extracting information about the functional state of an organism. For this reason, such recordings are widely used as tools for supporting medical decision. Nevertheless, reaching a diagnostic decision based on biosignal recordings normally requires analysis of long data records by specialized medical personnel. In several cases, specialized medical attention is unavailable, due to the high quantity of patients and data to analyze. Besides, the access to this kind of service may be difficult in remote places. As a result, the quality of medical service is deteriorated. In this sense, automated decision support systems are an important aid for improving pathology diagnosis and treatment, specially when long data records are involved. The successful performance of automatic decision systems strongly depends on the adequate choice of features. Therefore, non-stationarity is one of the most important problems to take into account. Non-stationarity is an inherent property of biosignals, as the underlying biological system has a time dependant response to environmental excitations. Specially, changes in physiological conditions and pathologies may produce significant variations. It has been found that non-stationary conditions give rise to changes in the spectral content of the biosignal (Hassanpour et al., 2004; Quiceno-Manrique et al., 2010; Sepúlveda-Cano et al., 2011; Subasi, 2007; Tarvainen et al., 2009; Tzallas et al., 2008). Therefore, time-frequency (t–f) features have been previously proposed for examining the dynamic properties of the spectral parameters during transient physiological or pathological episodes. It is expected that t–f features reveal the correlation between the t–f characteristics of abnormal non-stationary behavior (Debbal & Bereksi-Reguig, 2007). For this reason, t–f methods should outperform conventional methods of frequency analysis (Tzallas et al., 2008). Besides, t–f features are expected to behave slowly enough along the time axis, so the usual stationary restrictions imposed on short-term intervals should work out better than when straightforwardly analyzing over the raw input biosignal. Among t–f features, time-frequency representations (TFR) are one of the most common and widely used features. TFRs are the most complete characterization method for non-stationary biosignals, as they display the energy distribution of a signal in time and frequency domains. Several TFR estimators have been proposed, which can be classified as non-parametric (linear and quadratic) and parametric (Marchant, 2003; Poulimenos & Fassois, 2006). The selection of 13