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• W4366669163 abstract "Fetal heart rate (fHR) is critical for assessing fetal health and diagnosing disorders, such as fetal distress, congenital heart disease, and intrauterine growth retardation. With the rapid development of the Internet of Medical Things (IoMT), fetal R-peak detection plays an important role in diagnosing heart defects during pregnancy. However, due to the nonlinear mixing of multiple sources in the noninvasive signals and the low signal-to-noise ratio (SNR), it is difficult to obtain accurate R-peak detection result. This article presents a dual self-calibrating system based on a spectral attention kernel independent component analysis (SA-KICA) module and a self-calibrating fetal R-peak detection (SC-FRD) module. SA-KICA is an ICA-based calibration module constructed by the spectral attention mechanism, which was sought from short-time Fourier transform (STFT) and was shipped back to original signal with convolution to achieve perfect maternal electrocardiogram (MECG) separation in high-dimensional linear separable space. Then, a periodic and morphological-based channel selector is designed to select the optimal MECG. After MECG removal, to further improve the performance of fetal R-peak detection, the SC-FRD module is introduced to utilize the interior peak information and self-calibrating strategy, which includes variance-based fetal R-peak seed selection, time-varying coarse prediction, and adaptive probability mask calibration. The proposed framework is a primary attempt to concurrently introduce the nonlinear feature, spectral information, and self-calibrating strategy in the field of fetal ECG processing. The framework achieved excellent performance in fetal R-peak detection accuracy on a simulated data set and two public data sets with varying divergence and richness of resources. The experimental results show that our framework is superior to existing methods and can be used as a potential fetal monitoring method in the application of IoMT. The code is released in <uri xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>https://github.com/bfyjr/NI-FECG-Extraction</uri> ." @default.
• W4366669163 created "2023-04-23" @default.
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• W4366669163 date "2023-09-15" @default.
• W4366669163 modified "2023-10-14" @default.
• W4366669163 title "A Dual Self-Calibrating Framework for Noninvasive Fetal ECG R-Peak Detection" @default.
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• W4366669163 doi "https://doi.org/10.1109/jiot.2023.3269096" @default.
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