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A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for ComplexPhysiologic Signals,” Circulation, Vol. 101, No. 23, 2000, pp. e215-e220. http://circ.ahajournals.org/cgi/content/full/101/23/e215

has been cited by the following article:

• TITLE: Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System

  AUTHORS: Cafer Avci, Gökhan Bilgin

  KEYWORDS: Sleep Apnea; Wavelet Decomposition; Adaptive Neuro Fuzzy Inference System

  JOURNAL NAME: Engineering, Vol.5 No.10B, October 31, 2013

  ABSTRACT: This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10th and 11th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. However, the best result is obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of entire data.