An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings
[ X ]
Tarih
2011
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 8 h 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. (C) 2011 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Heart Rate Variability (Hrv), Ecg-Derived Respiration (Edr), Discrete Wavelet Transform (Dwt), Fast-Fourier Transform (Fft), Least Square Support Vector Machine (Ls-Svm), Obstructive Sleep Apnea (Osa)
Kaynak
Expert Systems With Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
38
Sayı
10
