Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques

Abstract

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals

“…In this simulation, we used three statistical measures, the sensitivity (SE), the accuracy (ACC), and the positive predictive value (PPV), in order to evaluate the performance of the proposed algorithm in detecting the FECG peaks [ 4 , 7 , 29 , 54 ]. We used the same real ECG Data, from Physionet Challenge 2013 data set a, and the simulation carried out in Section 5.2 .…”

“…However, the amplitude and duration of P, QRS, and T waves are different. In addition, the FECG signal has higher frequency than the MECG signal [ 2 , 4 ]. The ECG signal is captured by appropriate electrodes placed at the abdominal and thorax.…”

“…The electrocardiogram (ECG) signal, in a non-invasive method, incorporates of the maternal ECG (MECG) signal, the fetal ECG (FECG) signal, and several sources of interference, such as power line interference, baseline wander, motion artifact, fetal brain activity, muscle artifact, as well as noise, such as instrumentation noise [ 1 , 2 , 3 ]. FECG signal is used to monitor the health status of the fetus by determining its maturity level, reactivity, development, and existence of fetal distress [ 4 ].…”

Efficient implementation of LMS adaptive filter‐based FECG extraction on an FPGA

“…Various techniques [1] involving statistical and time‐domain analysis have been exploited to extract the FECG. Adaptive filtering [2], non‐linear decomposition [3], blind source separation [4] (independent component analysis (ICA)‐based methods), wavelet transform (WT)‐based techniques [5–7], neural network‐based approaches [8] are widely used.…”

Arquitetura de Hardware Baseada no Algoritmo NLMS para a Extração de Sinais FECG

“…O monitoramento continuo do sinal FECG permite a detecção de doenças de distúrbios cardiovasculares com maior índice de acerto. A falta de uma monitoração contínua inviabiliza a rápida observação e ação sobre as patologias, resultando na principal causa de morte fetal [3]. Considerando esse cenário, a busca por um dispositivo portátil, capaz de extrair um sinal de FECG puro, tem-se tornado um tema atrativo de pesquisa, e que tem sido explorado em ternos de hardware.…”