Fetal Heart Sounds Detection Using Wavelet Transform and Fractal Dimension

Abstract

Phonocardiography is a non-invasive technique for the detection of fetal heart sounds (fHSs). In this study, analysis of fetal phonocardiograph (fPCG) signals, in order to achieve fetal heartbeat segmentation, is proposed. The proposed approach (namely WT-FD) is a wavelet transform (WT)-based method that combines fractal dimension (FD) analysis in the WT domain for the extraction of fHSs from the underlying noise. Its adoption in this field stems from its successful use in the fields of lung and bowel sounds de-noising analysis. The efficiency of the WT-FD method in fHS extraction has been evaluated with 19 simulated fHS signals, created for the present study, with additive noise up to (3 dB), along with the simulated fPCGs database available at PhysioBank. Results have shown promising performance in the identification of the correct location and morphology of the fHSs, reaching an overall accuracy of 89% justifying the efficacy of the method. The WT-FD approach effectively extracts the fHS signals from the noisy background, paving the way for testing it in real fHSs and clearly contributing to better evaluation of the fetal heart functionality.

Keywords: fetal heart rate; fetal heart sound; fetal phonocardiogram; fractal dimension thresholding; wavelet transform.

Figure 1

Wavelet transform (WT) decomposition on a simulated fetal phonocardiograph signal. (A) The original signal X[n]. (B–H) The seven decomposed levels of the input signal.

Figure 2

A working example of the production procedure of the binary thresholds ${\text{SBTH}}_3^1$ and ${\text{NBTH}}_3^1$ derived from the application of the wavelet transform (WT)–fractal dimension (FD) filter to a case of fetal phonocardiograph recording. These results refer to scale j = 3 and iteration k = 1 during the application of the WT–FD filter to the input signal. (A) WT₃, the third WT coefficient, (B) ${\text{FD}}_3^1$, the estimated FD using Katz’s definition by Eq. 2, (C) ${\text{FDPP}}_3^1$, the output of the FD-peak peeling algorithm, (D) ${\text{SBTH}}_3^1$, the signal binary threshold, and (E) ${\text{NBTH}}_3^1$, the noise binary threshold.

Figure 3

Experimental result from the application of the wavelet transform–fractal dimension scheme to simulated fetal phonocardiograph signal. (A) X[n] represents a section of 5,000 samples of a normal heart rate case with unexpected robust noise. (B) X_REC[n] corresponds to the normalized treated signal without the overlap of noise. The arrows indicate the location of the S2 sound that the algorithm efficiently reveals.

Figure 4

Analysis results when wavelet transform (WT)–fractal dimension (FD) filter is applied to a part of real data. (A) A time section of 3 s of the real fetal phonocardiograph recording, with maternal heart rate of 96 bpm and fetal heart rate of 145 bpm. (B) The fourth level of the estimated WT coefficients selected for the detection of the fetal heart sounds (fHSs). (C) The result of the de-noised fHS signal after the final WT–FD analysis with S1 and S2 denoting the first and second fHS, respectively.