Abstract
Abstract 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.
Highlights
Biosignal recordings are useful to extract information about the functional state of the human organism
For example, the possibility to automatically detect epileptic seizures from EEG signals is limited by the wide variety of frequencies, amplitudes, spikes, and waves that use to appear [5] along the time with no precise localization
This section analyzes the tuning of the parameters that characterize the methods proposed: the number of neighbors of the k-nearest neighbors (k–NN) classifier, the number of components used by the linear decomposition approaches, and the relevance threshold
Summary
Biosignal recordings are useful to extract information about the functional state of the human organism. For this reason, such recordings are widely used to support the diagnosis, making automatic decision systems important tools to improve the pathology detection and its evaluation. PCG recordings are non-stationary signals that exhibit sudden frequency changes and transients [3]. In another example, in the aforementioned examples, the conventional analysis in time or frequency domains does not sufficiently provide relevant information for feature extraction and classification, limiting an automatic analysis for diagnostic purposes. The main difficulty to automatically detect physiological or pathological conditions lies in the wide variety of patterns that use to appear in non-stationary conditions. A current challenging problem is to detect a variety of non-stationary biosignal activities with a low computational complexity, to provide tools for efficient biosignal databases management and annotation
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