Extraction of Fetal ECG Signal from the Single Channel Abdominal ECG Signal Recording

The popular technique used for detection of fetal heart rate before delivery is Fetal Electrocardiogram (FECG). It shows the muscular function and electrical activity of the fetus heart. It represents the characteristics such as dynamic behaviors, waveform and heart rate of the fetus. These characteristics help to determine the fetal development, the existence of fetal distress, fetal life, fetal maturity or congenital heart disease. These above characteristics help the doctors for appropriate treatment during pregnancy. The heart rate of the fetus can easily be detected after estimation of the fetal ECG signal from the abdominal ECG signal. The abdominal ECG signal is collected by placing electrode at the abdomen area of the mother. The abdominal ECG signal contains fetal ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal from the abdominal ECG signal, removal of the noise and the maternal ECG component presented in it is very much necessary. Maternal ECG component is the dominant part of the abdominal ECG signal. To remove the maternal ECG component present in the abdominal ECG signal, accurate detection of the maternal R peaks from the abdominal ECG signal is required. An efficient R peak detection technique is required for the detection of accurate maternal R peaks from the abdominal ECG signal and the accurate detection of R peaks of the extracted fetal ECG signal as well. So that heart rate of the fetus can be computed. However, almost all existing R peak detectors suffer due to the non-stationary behavior of both QRS morphology and noise. To overcome these difficulties, we have proposed a three-stage improved method to detect R peaks based on Shannon energy envelope. The proposed R peak detection method in this dissertation shows improved performance compared to other existing methods available in the literature. In the recent years, Extended Kalman Smoother (EKS) has been used and has shown good performance for extraction of the fetal ECG signal from the single channel abdominal ECG signal. But the limitation of this method is that it fails to extract fetal QRS complex if it is overlapped by the maternal QRS complex in the abdominal ECG signal. The method also sometimes requires operator’s interaction to initialize the parameter of EKS to extract the fetal ECG signal which is dependent on abdominal ECG signal for better performance. Author of this thesis has investigated the effectiveness of Adaptive Neuro-Fuzzy Inference System (ANFIS) with EKS for extraction of the fetal ECG signal using single channel abdominal ECG signal. The EKS with ANFIS method proposed in this work for fetal ECG extraction is found to detect fetal QRS complex even if it is overlapped by the maternal QRS complex in the abdominal ECG signal. In the EKS with ANFIS framework, proposed Shannon energy based R peak detection is used for detection of the maternal R peaks from the abdominal ECG signal. The EKS filtering framework for denoising purpose requires operator’s interaction. To avoid the operator’s interaction and also to provide better performance using EKS framework, author has investigated the effectiveness of the Extended Kalman Smoother (EKS) with the Differential Evolution (DE) technique for noise cancellation of the ECG signal. DE is used as an automatic parameter selection method for the selection of 10 optimized parameters of the ECG signal, and these are used to create the ECG signal according to the real ECG signal. Also, these parameters are used in the EKS algorithm for the development of the state equation and initialization of the parameters of the EKS. The EKS framework is used for denoising of the ECG signal from the single channel recording. The effectiveness of the proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better Signal to Noise Ratio (SNR) improvement, lesser Mean Square Error (MSE) and Percent of Root mean square Distortion (PRD) compared to other well-known methods. Finally, the author has proposed a five-stage based methodology for further improvement of extracted FECG from the single channel abdominal ECG using DE algorithm, EKS and ANFIS framework. The pre-processing stage is used to remove the noise from the abdominal ECG signal and the EKS framework is used to estimate the maternal ECG signal from the abdominal ECG signal. The optimized parameters of the maternal ECG component (signal) are required to develop the state and measurement equation of the EKS framework and the same are selected by the DE algorithm. The relationship between the maternal ECG signal and the available maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the actual maternal ECG component present in the abdominal ECG signal and also to recognize this nonlinear relationship, the ANFIS is used. Inputs to the ANFIS framework are output of the EKS and the pre-processed abdominal ECG signal. The fetal ECG signal is computed by subtracting output of the ANFIS from the pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of 2013 physionet/computing in cardiology challenge database (PCDB) are used for validation of the proposed methodology. This thesis also describes a Field Programmable Gate Array (FPGA) implementation of a heart rate calculation system using Electrocardiogram (ECG) signal. The proposed FPGA based heart calculation system is FPGA implementation of proposed R peak detection technique based on Shannon energy envelope with a little modification. After heart rate calculation, tachycardia, bradycardia or normal heart rate can easily be detected. Heart rate is calculated by detecting the R peaks from the ECG signal. To provide a portable and the continuous heart rate monitoring system for patients needs a dedicated hardware. FPGA provides easy testability, allows faster implementation and verification option for implementing a new design. We have proposed a five-stage based methodology by using basic VHDL blocks like addition, multiplication and data conversion (real to the fixed point and vice-versa) etc for our proposed design. The proposed FPGA based heart rate calculation (R-peak detection) method shows better performance compared to other well-known methods for detection of R peaks (heart rate calculation) and successfully implemented in FPGA