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

Abstract

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.