Automated identification of abnormal fetuses using fetal ECG and doppler ultrasound signals

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...

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Bibliographic Details
Main Authors: Khandoker, A H, Kimura, Y, Palaniswami, M
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Electrocardiography
Fetus
Signal analysis
Signal processing
Signal resolution
Support vector machines
Timing
Ultrasonic imaging
Valves
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Summary: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 contents of the Doppler signals with the opening(o) and closing(c) of the heart's valves [Aortic (A) and Mitral(M)]. Five types of feature, namely 1) R-R intervals, 2) time intervals from R-wave of QRS complex of FECG to opening and closing of aortic valve, i.e. R-Ao 3) R-Ac 4) for the mitral valve R-Mc and 5) R-Mo were extracted from 60 beats and used as inputs to the SVM. Using leave-one-fetus out cross validation technique, an SVM with polynomial kernel (d=3, C=10) correctly recognized 8 abnormal (heart anomalies) fetuses out of 29 fetuses.
ISSN:0276-6574
2325-8853