Key result
The XCSR classifier system achieved 89.8% accuracy in diagnosing obstructive sleep apnea from ECG signals, outperforming ANFIS, SVM, and KNN classifiers.
Why the study?
Does a modified XCS classifier system using ECG-derived heart rate and respiratory signals improve the accuracy of diagnosing obstructive sleep apnea compared to other methods?
Does a modified XCS classifier system using ECG-derived heart rate and respiratory signals improve the accuracy of diagnosing obstructive sleep apnea compared to other methods?
An intelligent method using a modified XCS classifier on ECG-derived signals demonstrates high accuracy for diagnosing obstructive sleep apnea.
Obstructive sleep apnea is a common condition with serious neural-psychological complications and cardiovascular problems if not diagnosed and treated in time. Despite the importance of this disease in our country, it has not received much attention and there are few centers for evaluating patients suffering from it. In this article, an intelligent method is introduced for diagnosing obstructive sleep apnea that uses features extracted from changes in heart rate and respiratory signals in the ECG as input for training and testing the modified XCS classifier system. Comparison of results obtained from implementing the mentioned method with those of other methods on physionet database showed desirable performance and high accuracy of the proposed system in diagnosing obstructive sleep apnea.
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Pour et al. (2015) studied Obstructive sleep apnea (n=30). XCSR classifier system vs. SVM, ANFIS, and KNN classifiers was evaluated on Diagnostic accuracy (percentage of correct answers). The XCSR classifier system achieved 89.8% accuracy in diagnosing obstructive sleep apnea from ECG signals, outperforming ANFIS, SVM, and KNN classifiers.
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