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Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation
1Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India. sutharvs@gmail.com.
Insights
Continuous fetal ECG monitoring is crucial for early detection of birth defects. This study developed a portable device and signal processing algorithms to extract fetal ECG from maternal ECG, enabling long-term monitoring and analysis of fetal heart rate variability.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Congenital heart defects are a leading cause of birth defect-related mortality.
- Current fetal monitoring methods like Cardiotachography (CTG) are limited for long-term, continuous use.
- Non-invasive fetal electrocardiogram (fECG) monitoring is essential for early detection of fetal cardiac issues.
Purpose of the Study:
- To develop a portable hardware prototype for continuous fetal ECG monitoring.
- To implement and evaluate signal processing algorithms for extracting fECG from maternal ECG.
- To analyze fetal heart rate variability (HRV) for assessing fetal health status.
Main Methods:
- Designed and developed a prototype hardware for raw ECG signal acquisition.
- Employed two signal processing techniques: Least Mean Square (LMS) adaptive noise cancellation and Wavelet Transformation.
- Evaluated algorithms using simulated, database, and subject-acquired ECG signals.
Main Results:
- Successfully extracted fetal ECG signals using both LMS and Wavelet methods.
- Achieved high sensitivity and accuracy for fetal QRS detection (Adaptive: 96.83% sensitivity, 89.87% accuracy; Wavelet: 95.97% sensitivity, 88.5% accuracy).
- Analyzed time-domain parameters of fetal and maternal heart rate variability.
Conclusions:
- The developed prototype and signal processing algorithms enable effective continuous non-invasive fetal ECG monitoring.
- Both LMS and Wavelet techniques are viable for fECG extraction and HRV analysis.
- This approach supports early identification of fetal health issues and can aid in preventing stillbirths.
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.
