Time–frequency based feature selection for discrimination of non-stationary biosignals

• Published in: EURASIP journal on advances in signal processing Vol. 2012; no. 1; pp. 1 - 18
• Main Authors: D Martínez-Vargas, Juan, I Godino-Llorente, Juan, Castellanos‐Dominguez, Germán
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 09.10.2012; BioMed Central Ltd
• Subjects: Advances in Nonstationary Electrophysiological Signal Analysis and Processing; Classification; Engineering; Extrapolation; Frequency bands; Methodology; Pathology; Quantum Information Technology; Reduction; Redundant; Representations; Signal,Image and Speech Processing; Spintronics; Relevance Threshold; Partial Little Square; Relevance Measure; Probability Distribution Function; Short Time Fourier Transform
• ISSN: 1687-6180; 1687-6180
• DOI: 10.1186/1687-6180-2012-219

This research proposes a generic methodology for dimensionality reduction upon time–frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time–frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98 %. The results open the possibility to extrapolate the methodology to the study of other biosignals.