Complexity-Based Discrepancy Measures Applied to Detection of Apnea-Hypopnea Events

In recent years, an increasing interest in the development of discriminative methods based on sparse representations with discrete dictionaries for signal classification has been observed. It is still unclear, however, what is the most appropriate way for introducing discriminative information into the sparse representation problem. It is also unknown which is the best discrepancy measure for classification purposes. In the context of feature selection problems, several complexity-based measures have been proposed. The main objective of this work is to explore a method that uses such measures for constructing discriminative subdictionaries for detecting apnea-hypopnea events using pulse oximetry signals. Besides traditional discrepancy measures, we study a simple one called Difference of Conditional Activation Frequency (DCAF). We additionally explore the combined effect of overcompleteness and redundancy of the dictionary as well as the sparsity level of the representation. Results show that complexity-based measures are capable of adequately pointing out discriminative atoms. Particularly, DCAF yields competitive averaged detection accuracy rates of 72.57% at low computational cost. Additionally, ROC curve analyses show averaged diagnostic sensitivity and specificity of 81.88% and 87.32%, respectively. This shows that discriminative subdictionary construction methods for sparse representations of pulse oximetry signals constitute a valuable tool for apnea-hypopnea screening.