Key result
The proposed dimensionality reduction algorithm achieved over 99% classification accuracy for ischemia detection while reducing the feature space by over 99%.
A novel three-level dimensionality reduction algorithm for ECG signals achieved over 99% classification precision for detecting ischemic beats while enabling physiological interpretation.
May enhance real-time ECG ischemia detection efficiency; leaves open prospective clinical validation before adoption.
An effective data representation methodology on high-dimension feature spaces is presented, which allows a better interpretation of subjacent physiological phenomena (namely, cardiac behavior related to cardiovascular diseases), and is based on search criteria over a feature set resulting in an increase in the detection capability of ischemic pathologies, but also connecting these features with the physiologic representation of the ECG. The proposed dimension reduction scheme consists of three levels: projection, interpretation, and visualization. First, a hybrid algorithm is described that projects the multidimensional data to a lower dimension space, gathering the features that contribute similarly in the meaning of the covariance reconstruction in order to find information of clinical relevance over the initial training space. Next, an algorithm of variable selection is provided that further reduces the dimension, taking into account only the variables that offer greater class separability, and finally, the selected feature set is projected to a 2-D space in order to verify the performance of the suggested dimension reduction algorithm in terms of the discrimination capability for ischemia detection. The ECG recordings used in this study are from the European ST-T database and from the Universidad Nacional de Colombia database. In both cases, over 99% feature reduction was obtained, and classification precision was over 99% using a five-nearest-neighbor classifier (5-NN).
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Delgado‐Trejos et al. (2009) studied Ischemia (n=268). Dimensionality reduction algorithm (WT, PCA, Isodata, SFFS) vs. Other classification models was evaluated on Classification accuracy for ischemia detection. The proposed dimensionality reduction algorithm achieved over 99% classification accuracy for ischemia detection while reducing the feature space by over 99%.
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