Developing a New Cardiac Doppler Signal Processing Framework for Screening Foetal Well Being

  • Saeed Abdulrahman Alnuaimi

Student thesis: Doctoral Thesis

Abstract

Early diagnosis of the cardiac abnormalities during the pregnancy may reduce the risk of perinatal morbidity and mortality. Cardiotocography (CTG) is a means of recording the fetal heartbeat from the Doppler ultrasound (DUS) and the uterine contractions during the pregnancy and this method is commonly used to screen for fetal abnormalities. These measurements are captured by using an ultrasound transducer placed on the maternal abdomen. DUS, which is commonly used for monitoring the fetal heart rate, can also be used for identifying the event timings of fetal cardiac valve motions. In early-stage fetuses, the detected Doppler signal suffers from noise and signal loss due to the fetal movements and changing fetal location during the measurement procedure. The fetal cardiac intervals, which can be estimated by measuring the fetal cardiac event timings, are the most important markers of fetal development and wellbeing. To advance DUS-based fetal monitoring methods, several powerful and well-advanced signal processing and machine-learning methods have recently been developed. An overview about the existing techniques used in fetal cardiac activity monitoring and a comprehensive survey on fetal cardiac Doppler signal processing frameworks were provided in this thesis. Furthermore, this thesis proposed a novel, noninvasive technique which can be used to identify the fetal cardiac timing events based upon the analysis of fetal DUS (based upon 66 normalv subjects belonging to three differing age groups) which can later be used to estimate fetal cardiac intervals from a DUS signal. The foundation of this method is a decomposition method referred to as Swarm Decomposition (SWD) which makes it possible for the frequency contents of Doppler signals to be associated with cardiac valve motions. These motions include the opening (o) and closing (c) of Aortic (A) and Mitral (M) valves. When compared to the empirical mode decomposition (EMD), SWD results achieve an excellent isolation of the constituent parts of analyzed DUS signals. Pulsed Doppler images are used in order to verify the estimated timings. Three fetal age groups were assessed in terms of their cardiac intervals: 16-29, 30-35, and 36-41 weeks. The proposed method was used to estimate the following fetal cardiac intervals: Systolic Time Interval (STI), Isovolumic Relaxation Time (IRT), Preejection Period (PEP), and Ventricular Ejection Time (VET). The evaluation of fetal cardiac performance can be enhanced, given that these findings can be leveraged as sensitive markers throughout the process. Moreover, this thesis investigates features derived from cepstrum of the fetal cardiac Doppler signals and use them to identify the fetal's cardiac time intervals on both normal and abnormal fetuses. A hybrid model of both swarm decomposition technique and the cepstral analysis was designed to enhance the accuracy of the fetal's cardiac time intervals identification.
Date of AwardMay 2019
Original languageAmerican English

Keywords

  • Swarm decomposition
  • Cepstrum analysis
  • Fetal Doppler ultrasound
  • Fetal cardiac intervals
  • Fetal monitoring

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