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.