The electrocardiogram (ECG) is a technique of recording bioelectric currents generated by the heart. Clinicians can evaluate the conditions of a patient's heart from the ECG and perform further diagnosis. ECG records are obtained by sampling the bioelectric currents sensed by several electrodes. LabVIEW with its signal processing capabilities provides you a robust and efficient environment for resolving ECG signal processing problems. To Learn more see LabVIEW for ECG Signal Processing.
This application note demonstrates how to use LabVIEW's powerful tools in denoising, analyzing, and extracting ECG signals from fetal electrocardiograms (FECG).
Fetal ECG Extraction
The fetal electrocardiogram (FECG) can be derived from the maternal abdominal ECG (AECG) and be used for the extraction of fetal heart rate (FHR), which indicates the cardiac condition of the fetus. The locations of leads for an 8-channel maternal ECG acquisition system are shown in Figure 10. Maternal thorax ECG signals are sampled from thorax leads while maternal abdominal ECG signals are obtained from abdominal leads. This section uses the following two methods to extract fetal heart rate: Independent Component Analysis (ICA) and adaptive filtering.
Figure 10: locations of lead sensors for 8-channel maternal ECG acquisition system (including 5 abdominal leads and 3 thorax leads)
1. ICA Approach
ICA is a method for retrieving independent hidden signals from a multi-channel observation. Assume that the observation X is a superposition of the source signal S, you can write:
X = AS
where A denotes a mixing matrix. The assumption of statistical independence of the signals S allows you to estimate both unknowns, S and A, from the observation X. Here, the maternal ECG and the fetal ECG can be treated as independent components i.e. they are parts of S, while the 8-channel measured ECG records constitute X. By performing ICA, you can obtain the estimation of S, which means you can obtain both maternal ECG and fetal ECG.
The ASPT provides the TSA Independent Component Analysis VI with which you can easily build an FHR extraction application as shown in Figure 11. In Figure 11, 8-channel ECG signals (acquired by the 8-channel system mentioned before) are the inputs of the ICA function, and the fetal ECG signal can be obtained from the output as one of the independent components. Figure 12 shows one maternal abdominal ECG and one fetal ECG extracted from ICA, respectively. From the fetal ECG, you can see that the maternal ECG has been suppressed effectively and the FHR can be obtained accurately and conveniently.
Figure 11: ICA method for fetal heart rate extraction
Figure 12: FHR extraction using ICA
2. Adaptive Filtering Approach
An adaptive filter is a filter that self-adjusts its coefficients to optimize a specified performance index. Adaptive filters have been used in a wide range of applications, one of which is adaptive noise cancellation, as shown in Figure 13.
Figure 13: Adaptive noise cancellation structure
In this example, the coefficients of the adaptive filter are adjusted by minimizing errors between the reference signal and the input noisy signal so that the fetal ECG can be obtained by subtracting the maternal thorax signal from the maternal abdominal signal. The LabVIEW implementation of this approach is shown in Figure 14.
Figure 14: Adaptive filtering method for FHR extraction
As shown in Figure 15, the adaptive filter with an appropriate order and step size can track and predict the maternal ECG and extract the fetal ECG as the prediction error.
Figure 15: FHR extraction using adaptive filter
LabVIEW and the signal processing-related toolkits can provide you a robust and efficient environment and tools for resolving ECG signal processing problem. This application note has demonstrated how to use these powerful tools in denoising, analyzing, and extracting ECG signals, LabVIEW can be also used in other biomedical signal processing applications such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG).
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 S. Z. Mahmoodabadi, A. Ahmadian, M.D. Abolhasani, M. Eslami, J. H. Bidgoli, “ECG Feature Extraction Based on Multiresolution Wavelet Transform”, IEEE Engineering in Medicine and Biology, 27th Annual Conference.
 Bert-Uwe Kohler, Carsten Henning, Reinhold Orglmeister, “The Principles of Software QRS Detection”, IEEE, Engineering in Medicine and Biology, Jan/Feb 2002.
 Donghui Zhang, “Wavelet Approach for ECG Baseline Wander Correction and Noise Reduction”, Proceedings of the 2005 IEEE, Engineering in Medicine and Biology 27th Annual Conference.
 Gari D. Clifford, Francisco Azuaje and Patrick McSharry, “Advanced Methods and Tools for ECG Data Analysis”, Artech House Publisers.
 Behzad Mozaffary, Mohammad A. Tinati, “ECG Baseline Wander Elimination using Wavelet Packets”, Transactions on engineering, computing and technology, V3, Dec, 2004.
 Ping Gao, Ee-Chien Chang, Lonce Wyse, “Blind Separation of Fetal ECG From Single Mixture Using SVD and ICA”.
 Kamran Jamshaid, Omar Akram, Farooq Sabir, Dr. Syed Ismail Shah, Dr. Jamil Ahmed, “Application of Adaptive and Non Adaptive Filters in ECG Signal Processing”.
Originally Submitted By: Greg Crouch, National Instruments