Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Spectrum Estimation and Adaptive Denoising of Fetal Electrocardiographic Signal


Affiliations
1 Department of ECE, SNS College of Technology, India
     

   Subscribe/Renew Journal


Fetal monitoring during pregnancy is important to support medical decision making. The fetal electrocardiogram (fECG) is a valuable signal to diagnose fetal well-being. Noninvasive recording of the fECG is performed by positioning electrodes on the maternal abdomen. A method of time-varying parametric spectrum estima­tion from ECG sequences is presented. Model parameters are estimated by extracting the time varying parameters and state variables of an ECG sequence. We consider the noisy time sequence generated by nonlinear autoregression, when the observations of the series contain measurement noise in addition to the signal. The spectrum estimates for each time instant then are obtained from the estimated model parameters.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 216

PDF Views: 2




  • Spectrum Estimation and Adaptive Denoising of Fetal Electrocardiographic Signal

Abstract Views: 216  |  PDF Views: 2

Authors

K. Boopathi Raja
Department of ECE, SNS College of Technology, India
R. Karthik
Department of ECE, SNS College of Technology, India
N. Bhuvaneshwari
Department of ECE, SNS College of Technology, India

Abstract


Fetal monitoring during pregnancy is important to support medical decision making. The fetal electrocardiogram (fECG) is a valuable signal to diagnose fetal well-being. Noninvasive recording of the fECG is performed by positioning electrodes on the maternal abdomen. A method of time-varying parametric spectrum estima­tion from ECG sequences is presented. Model parameters are estimated by extracting the time varying parameters and state variables of an ECG sequence. We consider the noisy time sequence generated by nonlinear autoregression, when the observations of the series contain measurement noise in addition to the signal. The spectrum estimates for each time instant then are obtained from the estimated model parameters.