Fetal QT Interval Estimation Using Sequential Hypothesis Testing.
Objective: Recent studies utilizing fetal magnetocardiography have demonstrated the efficacy of corrected QT interval (QTc) measurement for in utero diagnosis and prognosis of long QT syndrome, a leading cause of sudden death in early life. The objective of the study was to formulate and test a novel statistical estimation method to detect the end of the fetal T-wave and thereby improve the accuracy of fetal QT interval measurement.Methods: To detect the end of the T-wave, we apply a sequential composite hypothesis test to decide when the T-wave has returned to baseline. The method uses the generalized likelihood ratio test in conjunction with a low-rank spatiotemporal model that exploits the repetitive nature of cardiac signals. The unknown model parameters are determined using maximum likelihood estimation.Results: In realistic simulations, the detector was shown to be accurate to within 10 ms (95% prediction interval), even at noise-to-signal ratios as high as 6. When applied to real data from normal fetuses, the detector agreed well with measurements made by cardiologists ( 1.4 6.9 ms).Conclusions: The method was effective and practical. Detector performance was excellent despite the continual presence of strong maternal interference.Significance: This detector serves as a valuable adjunct to traditional measurement based on subjective assessment.Objective: Recent studies utilizing fetal magnetocardiography have demonstrated the efficacy of corrected QT interval (QTc) measurement for in utero diagnosis and prognosis of long QT syndrome, a leading cause of sudden death in early life. The objective of the study was to formulate and test a novel statistical estimation method to detect the end of the fetal T-wave and thereby improve the accuracy of fetal QT interval measurement.Methods: To detect the end of the T-wave, we apply a sequential composite hypothesis test to decide when the T-wave has returned to baseline. The method uses the generalized likelihood ratio test in conjunction with a low-rank spatiotemporal model that exploits the repetitive nature of cardiac signals. The unknown model parameters are determined using maximum likelihood estimation.Results: In realistic simulations, the detector was shown to be accurate to within 10 ms (95% prediction interval), even at noise-to-signal ratios as high as 6. When applied to real data from normal fetuses, the detector agreed well with measurements made by cardiologists ( 1.4 6.9 ms).Conclusions: The method was effective and practical. Detector performance was excellent despite the continual presence of strong maternal interference.Significance: This detector serves as a valuable adjunct to traditional measurement based on subjective assessment.
Department of Radiation OncologyUniversity of Rochester Medical Center.
Suhong
S
Yu
Department of Electrical and Computer EngineeringUniversity of Wisconsin-Madison.
Barry D
BD
Van Veen
Department of Medical PhysicsUniversity of Wisconsin-Madison.
William J
WJ
Lutter
Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705USA.
Ronald T
RT
Wakai
D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D008133:Long QT Syndrome / Q000175:diagnosis; D053798:Magnetocardiography / Q000379:methods*; D011247:Pregnancy; D011296:Prenatal Diagnosis / Q000379:methods*; D012815:Signal Processing, Computer-Assisted
Biomedical measurement; Covariance matrices; Detectors; Interference; Maximum likelihood estimation; Testing