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This work aims to develop a minimally obtrusive simple system for the automatic detection and classification of sleep apnea (SA) events using the single-channel abdomen respiratory effort signal (Abd RES) taken from the ISRUC sleep database. The discrete wavelet transform energy, time domain respiratory rate variability and the Shannon entropy features are extracted from the Abd RES to build a classifier system. The performance of linear multi-class support vector machine (SVM) and random forest (RF) classifiers is compared for the application. Furthermore, an improved random forest (IRF) classifier is proposed, which is a combination of data re-sampling, RF algorithm and threshold moving method. It is designed to deal with the imbalance in the input dataset for better classification results. Various systems are designed using individual feature sets, but classification accuracy increases when feature sets are combined. A maximum accuracy of 89.66% is achieved with the IRF based system using reduced feature set obtained by principal component analysis. Thus, the proposed method can provide an effective, economical and accessible screening tool for SA.

Respiratory Effort Signal Based Sleep Apnea Detection System Using Improved Random Forest Classifier