An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

• Published in: Expert systems with applications Vol. 38; no. 10; pp. 12880 - 12890
• Main Authors: Yildiz, Abdulnasir, Akın, Mehmet, Poyraz, Mustafa
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.09.2011
• Subjects: Algorithms; Diagnosis; Discrete wavelet transform (DWT); Disorders; ECG-derived respiration (EDR); Expert systems; Fast-Fourier transform (FFT); Heart rate variability (HRV); Least square support vector machine (LS-SVM); Obstructive sleep apnea (OSA); Patients; Recognition; Recording; Sleep; Heart rate variability (HRV); Discrete wavelet transform (DWT); Fast-Fourier transform (FFT); Least square support vector machine (LS-SVM); ECG-derived respiration (EDR); Obstructive sleep apnea (OSA)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2011.04.080

► DWT based analysis of EKG recordings. ► FFT based feature extraction. ► Feature space reduction. ► LS-SVM based classification. Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings.