Wavelet filtering of fetal phonocardiography: A comparative analysis

Selene Tomassini; Annachiara Strazza; Agnese Sbrollini; Ilaria Marcantoni; Micaela Morettini; Sandro Fioretti; Laura Burattini; Selene Tomassini; Annachiara Strazza; Agnese Sbrollini; Ilaria Marcantoni; Micaela Morettini; Sandro Fioretti; Laura Burattini

doi:10.3934/mbe.2019302

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 6034-6046. doi: 10.3934/mbe.2019302

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Research article Special Issues

Wavelet filtering of fetal phonocardiography: A comparative analysis

1.
Cardiovascular Bioengineering Lab, Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
2.
Laboratorio di Bioingegneria, Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy

Received: 26 March 2019 Accepted: 13 June 2019 Published: 29 June 2019

Fetal heart rate (FHR) monitoring can serve as a benchmark to identify high-risk fetuses. Fetal phonocardiogram (FPCG) is the recording of the fetal heart sounds (FHS) by means of a small acoustic sensor placed on maternal abdomen. Being heavily contaminated by noise, FPCG processing implies mandatory filtering to make FPCG clinically usable. Aim of the present study was to perform a comparative analysis of filters based on Wavelet transform (WT) characterized by different combinations of mothers Wavelet and thresholding settings. By combining three mothers Wavelet (4^th-order Coiflet, 4^th-order Daubechies and 8^th-order Symlet), two thresholding rules (Soft and Hard) and three thresholding algorithms (Universal, Rigorous and Minimax), 18 different WT-based filters were obtained and applied to 37 simulated and 119 experimental FPCG data (PhysioNet/PhysioBank). Filters performance was evaluated in terms of reliability in FHR estimation from filtered FPCG and noise reduction quantified by the signal-to-noise ratio (SNR). The filter obtained by combining the 4^th-order Coiflet mother Wavelet with the Soft thresholding rule and the Universal thresholding algorithm was found to be optimal in both simulated and experimental FPCG data, since able to maintain FHR with respect to reference (138.7[137.7; 140.8] bpm vs. 140.2[139.7; 140.7] bpm, P > 0.05, in simulated FPCG data; 139.6[113.4; 144.2] bpm vs. 140.5[135.2; 146.3] bpm, P > 0.05, in experimental FPCG data) while strongly incrementing SNR (25.9[20.4; 31.3] dB vs. 0.7[-0.2; 2.9] dB, P < 10^-14, in simulated FPCG data; 22.9[20.1; 25.7] dB vs. 15.6[13.8; 16.7] dB, P < 10^-37, in experimental FPCG data). In conclusion, the WT-based filter obtained combining the 4^th-order Coiflet mother Wavelet with the thresholding settings constituted by the Soft rule and the Universal algorithm provides the optimal WT-based filter for FPCG filtering according to evaluation criteria based on both noise and clinical features.
- fetal monitoring,
- fetal phonocardiography,
- mothers wavelet,
- thresholding settings,
- wavelet transform filtering
Citation: Selene Tomassini, Annachiara Strazza, Agnese Sbrollini, Ilaria Marcantoni, Micaela Morettini, Sandro Fioretti, Laura Burattini. Wavelet filtering of fetal phonocardiography: A comparative analysis[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 6034-6046. doi: 10.3934/mbe.2019302

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Abstract

Fetal heart rate (FHR) monitoring can serve as a benchmark to identify high-risk fetuses. Fetal phonocardiogram (FPCG) is the recording of the fetal heart sounds (FHS) by means of a small acoustic sensor placed on maternal abdomen. Being heavily contaminated by noise, FPCG processing implies mandatory filtering to make FPCG clinically usable. Aim of the present study was to perform a comparative analysis of filters based on Wavelet transform (WT) characterized by different combinations of mothers Wavelet and thresholding settings. By combining three mothers Wavelet (4^th-order Coiflet, 4^th-order Daubechies and 8^th-order Symlet), two thresholding rules (Soft and Hard) and three thresholding algorithms (Universal, Rigorous and Minimax), 18 different WT-based filters were obtained and applied to 37 simulated and 119 experimental FPCG data (PhysioNet/PhysioBank). Filters performance was evaluated in terms of reliability in FHR estimation from filtered FPCG and noise reduction quantified by the signal-to-noise ratio (SNR). The filter obtained by combining the 4^th-order Coiflet mother Wavelet with the Soft thresholding rule and the Universal thresholding algorithm was found to be optimal in both simulated and experimental FPCG data, since able to maintain FHR with respect to reference (138.7[137.7; 140.8] bpm vs. 140.2[139.7; 140.7] bpm, P > 0.05, in simulated FPCG data; 139.6[113.4; 144.2] bpm vs. 140.5[135.2; 146.3] bpm, P > 0.05, in experimental FPCG data) while strongly incrementing SNR (25.9[20.4; 31.3] dB vs. 0.7[-0.2; 2.9] dB, P < 10^-14, in simulated FPCG data; 22.9[20.1; 25.7] dB vs. 15.6[13.8; 16.7] dB, P < 10^-37, in experimental FPCG data). In conclusion, the WT-based filter obtained combining the 4^th-order Coiflet mother Wavelet with the thresholding settings constituted by the Soft rule and the Universal algorithm provides the optimal WT-based filter for FPCG filtering according to evaluation criteria based on both noise and clinical features.

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