Document Type: Original Research
PhD Candidate, Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
MSc, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
MSc, Department of Health Management and Economics, Tehran University of Medical Sciences, Tehran, Iran
PhD, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Background: Fetal heart rate (FHR) extracted from abdominal electrocardiogram (ECG) is a powerful non-invasive method in appropriately assessing the fetus well-being during pregnancy. Despite significant advances in the field of electrocardiography, the analysis of fetal ECG (FECG) signal is considered a challenging issue which is mainly due to low signal to noise ratio (SNR) of FECG.
Objective: In this study, we present an approach for accurately locating the fetal QRS complexes in non-invasive FECG.
Materials and Methods: In this experimental study, the proposed method included 4 steps. In step 1, comb notching filter was employed to pre-process the abdominal ECG (AECG). Furthermore, low frequency noises were omitted using wavelet decomposition. In next step, principal component analysis (PCA) and signal quality assessment (SQA) were used to obtain an optimal AECG reference channel for maternal R-peaks detection. In step 3, maternal ECG (MECG) was removed from mixture signal and FECG was extracted. In final step, the extracted FECG was first decomposed by discrete wavelet transforms at level 10. Then, by employing details of levels 2, 3, 4, the new FECG signal was reconstructed in which various noises and artifacts were removed and FECG components whose frequency were close to the fetal QRS complexes remained which increased the performance of the method.
Results: For evaluation, 15 recordings of PhysioNet Noninvasive FECG database were used and the average F1 measure of 98.77% was obtained.
Conclusion: The results indicate that use of both an efficient analysis of major component of AECG along with a signal quality assessment technique has a promising performance in FECG analysis.
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