Tomassini Selene, Strazza Annachiara, Sbrollini Agnese, Marcantoni Ilaria, Morettini Micaela, Fioretti Sandro, Burattini Laura
Cardiovascular Bioengineering Lab, Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
Laboratorio di Bioingegneria, Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
Math Biosci Eng. 2019 Jun 29;16(5):6034-6046. doi: 10.3934/mbe.2019302.
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-order Coiflet, 4-order Daubechies and 8-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-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 , in simulated FPCG data; 22.9[20.1; 25.7] dB vs. 15.6[13.8; 16.7] dB, P < 10, in experimental FPCG data). In conclusion, the WT-based filter obtained combining the 4-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.
胎儿心率（FHR）监测可作为识别高危胎儿的基准。胎儿心音图（FPCG）是通过放置在孕妇腹部的小型声学传感器记录胎儿心音（FHS）。由于FPCG受到严重噪声污染，其处理需要进行强制性滤波以使FPCG在临床上可用。本研究的目的是对基于小波变换（WT）的滤波器进行比较分析，这些滤波器具有不同的母小波和阈值设置组合。通过组合三种母小波（4阶Coiflet、4阶Daubechies和8阶Symlet）、两种阈值规则（软阈值和硬阈值）和三种阈值算法（通用、严格和极小极大），获得了18种不同的基于WT的滤波器，并将其应用于37个模拟和119个实验FPCG数据（PhysioNet/PhysioBank）。根据从滤波后的FPCG估计FHR的可靠性以及通过信噪比（SNR）量化的降噪效果来评估滤波器性能。发现在模拟和实验FPCG数据中，将4阶Coiflet母小波与软阈值规则和通用阈值算法相结合得到的滤波器是最优的，因为它能够相对于参考值保持FHR（在模拟FPCG数据中为138.7[137.7；140.8]次/分钟对140.2[139.7；140.7]次/分钟，P>0.05；在实验FPCG数据中为139.6[113.4；144.2]次/分钟对140.5[135.2；146.3]次/分钟，P>0.05），同时显著提高SNR（在模拟FPCG数据中为25.9[20.4；31.3]dB对0.7[-0.2；2.9]dB，P<10；在实验FPCG数据中为22.9[20.1；25.7]dB对15.6[13.8；16.7]dB，P<10）。总之，根据基于噪声和临床特征的评估标准，将4阶Coiflet母小波与由软规则和通用算法构成的阈值设置相结合得到的基于WT的滤波器为FPCG滤波提供了最优的基于WT的滤波器。
