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

Abstract

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.

Temporal complexity of EEG encodes human alertness

“…In information theory, the concept of entropy has been used to quantify the complexity of signals in the time domain (aka time series). This complexity has been related to both the signal's underlying structure and its information content (Rolón et al 2018). A signal with high entropy is characterized by highly unpredictable future values.…”

Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals

“…The detection of atoms containing useful discriminant information can be addressed in different ways. Among all existing alternatives, the most commonly used strategy consists of performing comparisons between conditional probability distributions [32,33]. In what follows, we proceed to describe two different criteria that shall be used in this article.…”

“…Based on the idea that the discriminant atoms of a dictionary are those more frequently used for representing signals belonging to a particular class, a measure called "Discriminative Conditional Activation Frequency" (DCAF) was proposed [3,33]. This measure was shown to be capable of efficiently quantifying the discriminability of the atoms in the context of a binary classification problem.…”

“…This new index was used for OSAH syndrome detection. Finally, each study was considered as pathological if the obtained AHI est was greater than a certain prescribed detection threshold [33].…”