Key result
The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method achieved the best results for fetal phonocardiography filtering, with an average S1 detection accuracy of 91.53% and S2 detection accuracy of 68.89%.
Why the study?
Fetal phonocardiography is non-invasive and low-cost, but different types of interference are sensed along with the desired signal.
Which single-channel signal processing method is most effective for filtering fetal phonocardiography signals?
Which single-channel signal processing method is most effective for filtering fetal phonocardiography signals?
The CEEMDAN algorithm provides superior filtering of fetal phonocardiography signals compared to other standard methods, improving the accuracy of fetal heart sound detection.
CEEMDAN may enhance fetal phonocardiography filtering in preclinical settings; leaves open human validation before clinical consideration.
Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.
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Barnova et al. (2022) studied Fetal phonocardiography signal processing (n=2). Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) vs. Other signal processing algorithms (S-G, FIR, AWT, MODWT, VMD, EMD, EEMD) was evaluated on Average accuracy in the detection of S1 sounds. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method achieved the best results for fetal phonocardiography filtering, with an average S1 detection accuracy of 91.53% and S2 detection accuracy of 68.89%.
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