A Method to Detect Obstructive Sleep Apnea Using Neural Network Classification of Time-Frequency Plots of the Heart Rate Variability

Author

Al-Abed, M. ; Manry, M. ; Burk, J.R. ; Lucas, E.A. ; Behbehani, Khosrow

Author_Institution

Univ. of Texas at Arlington, Arlington

fYear

2007

fDate

22-26 Aug. 2007

Firstpage

6101

Lastpage

6104

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 plusmn 9.38 years, AHI 3.75 plusmn 3.11) and 14 apneic subjects (8 males, 6 females; age 50.28 plusmn 9.60 years; AHI 31.21 plusmn 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.

Keywords

Monte Carlo methods; bioelectric phenomena; discrete Fourier transforms; electrocardiography; feature extraction; image classification; image texture; learning (artificial intelligence); matrix algebra; medical image processing; multilayer perceptrons; neurophysiology; pneumodynamics; sleep; time-frequency analysis; gray-level images; heart rate variability; image represention; mean training classification sensitivity; neural network classification; nocturnal ECG; normalized gray-level co-occurrence matrices; obstructive sleep apnea detection; randomized Monte-Carlo simulations; short-time discrete Fourier transform; sleep disordered breathing; textural features extraction; three-layer multilayer perceptron classifier; time-frequency plots; Data mining; Discrete Fourier transforms; Electrocardiography; Feature extraction; Heart rate detection; Heart rate variability; Image generation; Neural networks; Sleep apnea; Time frequency analysis; Artificial Neural Networks; Co-occurrence Matrix; ECG; Heart Rate Variability; Multi-Layer Perceptron; Sleep Disordered Breathing; Textural Features; Time-Frequency Plots; Algorithms; Diagnosis, Computer-Assisted; Electrocardiography; Expert Systems; Female; Heart Rate; Humans; Male; Middle Aged; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Sleep Apnea, Obstructive;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Conference_Location

Lyon

ISSN

1557-170X

Print_ISBN

978-1-4244-0787-3

Type

conf

DOI

10.1109/IEMBS.2007.4353741

Filename

4353741