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This study presents an automated and non-invasive technology using an integrated fetal transabdominal electrocardiogram system and Doppler cardiogram (DCG) to identify fetal heart anomalies. Multiresolution wavelet analysis and Jensen-Shannon divergence (JSD) methods were used to identify the frequency contents of the Doppler signals to be linked to the opening and closing of the heart's valves (Aortic...
In this study, we propose an automated algorithm (support vector machines, SVM) to recognize the abnormal fetus using the timings of fetal cardiac events on the basis of analysis of simultaneously recorded fetal ECG (FECG) and Doppler ultrasound (DUS) signal. FECG and DUS signals from 29 fetuses [21 normal and 8 abnormal] were analyzed. Multiresolution wavelet analysis was used to link the frequency...
In this study, we propose a non-invasive system to recognize the timings of fetal cardiac events on the basis of analysis of fetal ECG and Doppler ultrasound signal together. Fetal ECG was extracted from transabdominal ECG (10 normal subjects, 38-41 weeks of gestation) using blind source separation with the reference signal. Multiresolution wavelet analysis enabled the frequency contents of the Doppler...
Fetal ECG and Doppler ultrasound signals provide clinically significant information concerning the physiological state of a fetus. In this study, we propose a non-invasive system to recognize the timings of fetal cardiac events on the basis of analysis of fetal ECG and Doppler ultrasound signal together. Fetal ECG was extracted from transabdominal ECG (10 normal subjects, 38-41 weeks of gestation)...
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