UNASSIGNED: A novel method for the automatic diagnosis of @entity27 ( @entity27 ) from an electrocardiogram (ECG) is presented. This method aims to detect @entity27 utilizing exclusively ECG recordings during sleep and present a minute-by-minute signal processing technique. In the proposed algorithm, a wide range of features based on heart rate variability (HRV) and ECG-derived respiratory ( @entity26186 ) signals are considered. The novelty of this study arises from employing bispectral analysis to the HRV and @entity26186 signals in order to illustrate quadratic phase-coupling that can be observed among signal components with different frequencies. From this perspective, in the proposed algorithm, a new feature set based on a higher order spectrum of HRV and @entity26186 signals is introduced and it is utilized to extract information regarding their non-linearity and non-Gaussianity. This feature vector is then fed into the input of a least-square support vector machine classifier. To implement the proposed method, the @entity386 -ECG database, which contains 70 nocturnal ECG records gathered from volunteer @entity1 and @entity1 , is used in this work. Results obtained from cross-validating 35 data records show that the normal recordings could be separated from the apneic recordings with an accuracy of 95.57% and a sensitivity and specificity of 98.64% and 92.51%, respectively. In addition, 35 other records were used for a pure independent validation of the proposed method and the obtained accuracy, sensitivity and specificity was 94.12%, 93.46% and 94.79% respectively in @entity27 . The performance of our proposed technique is better than in other existing approaches. It can be used as a reliable tool for automatic @entity27 identification and as a result, it will improve medical services.

Obstructive sleep XXXX detection using spectrum and bispectrum analysis of single-lead ECG signal.

@entity26186 :: ('23089', 'Gene') :: ['EDR'] @entity1 :: ('9606', 'Species') :: ['women', 'men'] @entity27 :: ('MESH:D020181', 'Disease') :: ['obstructive sleep apnea', 'OSA episode detection', 'OSA'] @entity386 :: ('MESH:D001049', 'Disease') :: ['apnea']
@entity386 :: (MESH:D001049,Disease) :: ['apnea']
