Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and Discrete Wavelet Transforms with Signal Quality Estimation

سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 149

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شناسه ملی سند علمی:

JR_JBPE-11-2_009

تاریخ نمایه سازی: 30 دی 1402

چکیده مقاله:

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 ۴ steps. In step ۱, 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 ۳, 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 ۱۰. Then, by employing details of levels ۲, ۳, ۴, 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, ۱۵ recordings of PhysioNet Noninvasive FECG database were used and the average F۱ measure of ۹۸.۷۷% 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.

نویسندگان

Mohammad Javad Mollakazemi

PhD Candidate, Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

Farhad Asadi

MSc, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran

Mahsa Tajnesaei

MSc, Department of Health Management and Economics, Tehran University of Medical Sciences, Tehran, Iran

Ali Ghaffari

PhD, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran

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