An algorithm for fetal heart rate detection using wavelet transform

2012 ◽  
Author(s):  
Shashi Kala Nagarkoti ◽  
Balraj Singh ◽  
Manoj Kumar
Keyword(s):  
Heart Rate ◽  
Wavelet Transform ◽  
Fetal Heart Rate ◽  
Fetal Heart

Time-frequency analysis of fetal heartbeat fluctuation using wavelet transform

1998 ◽  
Vol 275 (6) ◽  
pp. H1993-H1999 ◽  
Author(s):  
Yoshitaka Kimura ◽  
Kunihiro Okamura ◽  
Takanori Watanabe ◽  
Nobuo Yaegashi ◽  
Shigeki Uehara ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Wavelet Transform ◽  
Gamma Distribution ◽  
Frequency Analysis ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Rate Fluctuation

We examined whether the nonlinear control mechanism of the fetal autonomic nervous system would change in various fetal states. Eight thousand or more fetal heartbeats were detected from normal, hypoxemic, and acidemic fetuses. Fetal heart Doppler-signal intervals were determined in a high-precision autocorrelation method, and a time series of fetal heart rate fluctuation was obtained. The distribution of the amplitude of temporal fluctuation in the low-frequency component of fetal heart rate frequency was studied using a method of time-frequency analysis called wavelet transform. Spline 4 was used as the mother wavelet function. A gamma distribution was observed from 17 wk of gestation onward. The value of the parameter ν of this gamma distribution was ∼1.6 and remained constant regardless of the gestational age or the time of day. The value of ν decreased significantly to 0.77 when the fetus developed acidemia and was 1.51 in hypoxemia and 1.54 in a normal condition. This study elucidates a nonlinear structure of the time series of heart rate fluctuation of the gamma distribution in the human fetus. This technique may provide a new quantitative index of fetal monitoring to diagnose fetal acidemia.

scholarly journals Classification of fetal abnormalities based on CTG signal

2014 ◽  
Vol 11 (2) ◽  
pp. 681-689
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Discrete Wavelet ◽  
Artificial Neural

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was transformed using transform domains Discrete Wavelet Transform(DWT) in order to obtain the system features .At the last stage the approximation coefficients result from the Discrete Wavelet Transform were fed to the Artificial Neural Networks and to the Fuzzy Logic, then compared between two results to obtain the best for classifying fetal heart rate.

scholarly journals Fetal Heart Rate Extraction Based on Wavelet Transform to Prevent Fetal Distress In Utero

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mengni Zhu ◽  
Liping Liu
Keyword(s):  
Heart Rate ◽  
Wavelet Transform ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Fetal Distress ◽  
In Utero ◽  
Control Group ◽  
Observation Group ◽  
Peak Point ◽  
Heart Monitoring

In order to improve the effective extraction of fetal heart rate and prevent fetal distress in utero, a study of fetal heart rate feature extraction based on wavelet transform to prevent fetal distress in utero was proposed. This paper adopts a fetal heart rate detection method based on the maximum value of the binary wavelet transform modulus. The method is simulated by the Doppler fetal heart signal obtained from the clinic. Compared with the original curve, the transformed curve can roughly see the change rule of the original signal and identify the peak point of the signal, but due to the large disturbance of the peak point, the influence on the computer processing is also great. The periodicity of the transformed signal is greatly enhanced, making it easier to deal with the computation. A total of 300 pregnant women with full-term fetal heart monitoring from January 2018 to January 2020 were selected as the research subjects and divided into the observation group and the control group. The observation group consisted of 100 patients with abnormal fetal heart monitoring, and the control group consisted of 200 patients with normal fetal heart monitoring. The uterine contractions and fetal heart rate were recorded, and the incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid and fecal contamination were observed. The incidence of fetal distress, cesarean section, neonatal asphyxia, and amniotic fluid fecal stain in the observation group were significantly higher than those in the control group. Fetal heart monitoring can accurately judge the situation of the fetus in pregnant women and timely diagnose the abnormal fetal heart rate, which has a better effect on the prognosis of perinatal infants and can reduce their mortality. It can effectively solve the problems existing in the autocorrelation algorithm and extract the fetal heart rate more accurately. It is an effective improved scheme of fetal heart rate extraction. It is very helpful in preventing fetal distress in utero.

Denoising of the fetal heart rate signal with non-linear filtering of the wavelet transform maxima

1997 ◽  
Vol 44 (3) ◽  
pp. 177-192 ◽  
Author(s):  
S. Papadimitriou ◽  
D. Gatzounas ◽  
V. Papadopoulos ◽  
V. Tzigounis ◽  
A. Bezerianos
Keyword(s):  
Heart Rate ◽  
Wavelet Transform ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Linear Filtering ◽  
Non Linear
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