Classification of sleep apnea through sub-band energy of abdominal effort signal using Wavelets + Neural Networks.
Detection and classification of sleep apnea syndrome (SAS) is a critical problem. In this study an efficient method for classification sleep apnea through sub-band energy of abdominal effort using a particularly designed hybrid classifier as Wavelets + Neural Network is proposed. The Abdominal respiration signals were separated into spectral sub-band energy components with multi-resolution Discrete Wavelet Transform (DWT). The energy content of these spectral components was applied to the input of the artificial neural network (ANN). The ANN was configured to give three outputs dedicated to SAS cases; obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). Through the network, satisfactory results that rewarding 85.62% mean accuracy in classifying SAS were obtained.
Tagluk
M Emin
ME
Department of Electrical and Electronics Engineering, University of Inonu, Malatya, Turkey.
Sezgin
Necmettin
N
D000005:Abdomen / Q000502:physiology*; D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012891:Sleep Apnea Syndromes / Q000145:classification*; D058067:Wavelet Analysis; D055815:Young Adult