A new method to detect obstructive sleep apnea using fuzzy classification of time-frequency plots of the heart rate variability.
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. Thirty selected features extracted from 10 different NGLCMs representing four characteristically different gray-level images are used as inputs to 10 Fuzzy Logic Systems (FLS) Classifiers. Each FLS is trained and their outputs are combined using a weighed majority rule method. The mean training detection sensitivity, specificity and accuracy are 86.87%, 71.72%, and 79.29%, respectively. The mean testing detection sensitivity, specificity and accuracy are 83.22%, 68.54%, and 75.88%, respectively.
Al-Abed
Mohammad
M
Bioengineering Department, University of Texas at Arlington and University of Texas SouthwesternMedical Center at Dallas, USA.
Behbehani
Khosrow
K
Burk
John R
JR
Lucas
Edgar A
EA
Manry
Michael
M
D000328:Adult; D004562:Electrocardiography; D005260:Female; D017143:Fuzzy Logic; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology