Name
Title | ||
|---|---|---|
A new method to detect obstructive sleep apnea using fuzzy classification of time-frequency plots of the heart rate variability. |
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. 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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IEMBS.2006.260880 | EMBC |
Keywords | DocType | Volume |
fourier transform,time frequency analysis,fuzzy logic,co occurrence matrix,feature extraction,fuzzy classification,time frequency,heart rate variability,majority rule,data mining,biomedical engineering | Conference | Suppl |
ISSN | Citations | PageRank |
1557-170X | 2 | 0.40 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Al-Abed | 1 | 2 | 0.40 |
| Khosrow Behbehani | 2 | 8 | 6.30 |
| John R Burk | 3 | 3 | 0.83 |
| Edgar A Lucas | 4 | 3 | 0.83 |
| Michael Manry | 5 | 2 | 0.40 |