A method to detect obstructive sleep apnea using neural network classification of time-frequency plots of the heart rate variability

Annu Int Conf IEEE Eng Med Biol Soc. 2007:2007:6102-5. doi: 10.1109/IEMBS.2007.4353741.

Abstract

This paper presents a new method of analyzing time-frequency plots of heart rate variability to detect sleep disordered breathing from nocturnal ECG. Data is collected from 12 normal subjects (7 males, 5 females; age 46+/-9.38 years, AHI 3.75+/-3.11) and 14 apneic subjects (8 males, 6 females; age 50.28+/-9.60 years; AHI 31.21+/-23.89). The proposed algorithm uses textural features extracted from normalized gray-level co-occurrence matrices (NGLCM) of images generated by short-time discrete Fourier transform (STDFT) of the HRV. Using feature selection, seventeen features extracted from 10 different NGLCMs representing four characteristically different gray-level images are used as inputs to a three-layer Multi-Layer Perceptron (MLP) classifier. After a 1000 randomized Monte-Carlo simulations, the mean training classification sensitivity, specificity and accuracy are 99.00%, 93.42%, and 96.42%, respectively. The mean testing classification sensitivity, specificity and accuracy are 94.42%, 85.40%, and 90.16%, respectively.

Publication types

  • Evaluation Study

MeSH terms

  • Algorithms*
  • Diagnosis, Computer-Assisted / methods*
  • Electrocardiography / methods*
  • Expert Systems
  • Female
  • Heart Rate*
  • Humans
  • Male
  • Middle Aged
  • Neural Networks, Computer*
  • Pattern Recognition, Automated / methods
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sleep Apnea, Obstructive / diagnosis*
  • Sleep Apnea, Obstructive / physiopathology*