A new method to detect obstructive sleep apnea using fuzzy classification of time-frequency plots of the heart rate variability
| Autor: | Mohammad A. Al-Abed, John R. Burk, Michael T. Manry, Khosrow Behbehani, Edgar A. Lucas |
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| Rok vydání: | 2006 |
| Předmět: |
Adult
Male Fuzzy classification Speech recognition Fuzzy logic Discrete Fourier transform Electrocardiography Fuzzy Logic Heart Rate medicine Humans Heart rate variability Mathematics Sleep Apnea Obstructive medicine.diagnostic_test business.industry Sleep apnea Pattern recognition Middle Aged medicine.disease Time–frequency analysis Obstructive sleep apnea Female Artificial intelligence business |
| Zdroj: | EMBC (Supplement) |
| DOI: | 10.1109/iembs.2006.260880 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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