An open source autocorrelation-based method for fetal heart rate estimation from one-dimensional Doppler ultrasound

Valderrama, Camilo E.; Stroux, Lisa; Katebi, Nasim; Paljug, Elianna; Hall-Clifford, Rachel; Rohloff, Peter; Marzbanrad, Faezeh; Clifford, Gari D.

doi:10.1088/1361-6579/ab033d

Cited by 16 publications

(34 citation statements)

References 38 publications

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“…For each 3.75-s window, the fetal heart rate (FHR) was estimated auto-correlation (AC)-based method using an open source code written in Matlab (MathWorks, Natick, MA, USA), previously introduced in (Valderrama et al, 2018b , 2019 ). Specifically, the method detects the fundamental period of the envelope of the 3.75-s window by applying auto-correlation, and then the FHR is estimated by dividing 60 between the fundamental period in seconds.…”

Section: Methodsmentioning

confidence: 99%

“…This sampling frequency has been shown to be sufficient for digital cardiotocography (Romagnoli et al, 2019), and corresponded to a Nyquist frequency of 2 Hz, thus allowing the extraction of spectral metrics in the range 0.03-1 Hz. For each 3.75-s window, the fetal heart rate (FHR) was estimated auto-correlation (AC)-based method using an open source code written in Matlab (MathWorks, Natick, MA, USA), previously introduced in (Valderrama et al, 2018b(Valderrama et al, , 2019. Specifically, the method detects the fundamental period of the envelope of the 3.75-s window by applying auto-correlation, and then the FHR is estimated by dividing 60 between the fundamental period in seconds.…”

Section: Deriving the Fhr Signalmentioning

confidence: 99%

“…Specifically, the method detects the fundamental period of the envelope of the 3.75-s window by applying auto-correlation, and then the FHR is estimated by dividing 60 between the fundamental period in seconds. More details of the FHR estimator are found in (Valderrama et al, 2019).…”

Section: Deriving the Fhr Signalmentioning

confidence: 99%

See 2 more Smart Citations

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Valderrama

Marzbanrad

Hall-Clifford

et al. 2020

Front. Artif. Intell.

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In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low-and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

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Section: Methodsmentioning

confidence: 99%

Section: Deriving the Fhr Signalmentioning

confidence: 99%

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A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Valderrama

Marzbanrad

Hall-Clifford

et al. 2020

Front. Artif. Intell.

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“…However, AC-based methods are affected by the inherent smoothing or averaging of the autocorrelation. Therefore, Valderrama et al introduced a reproducible and generalizable AC-based method for FHR estimation from one dimensional Doppler ultrasound (1D-DUS) signals taken with an inexpensive hand-held FHR detector [19]. Al-Angari et al proposed the use of empirical mode decomposition (EMD) of the 1D-DUS signals and the kurtosis of the instantaneous mode function as a measure of FHR.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ultrasonic Doppler Fetal Heart Rate Detection System Using Windowed Digital Demodulation

Dai

Zhao²,

Peng

et al. 2021

IEEE Access

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Fetal heart rate (FHR) is an essential indicator of fetal well-being. The ultrasonic Doppler FHR detector is widely used to monitor the fetus's health due to its advantages of non-invasion, high sensitivity, and good directivity. However, the existing commercial Doppler FHR detector primarily uses an analog multiplier for demodulation, which has limited functions and insufficient sensitivity. The analog demodulation is performed only on a single-sideband signal(in-phase or quadrature) (IQ), which is impossible to derive the fetal heart movement's vector velocity. Moreover, repetitive recognition of the mitral valve's positive and negative motion easily causes the measured FHR twice the actual one (FHR doubling). Although the traditional digital demodulation FHR detector can obtain in-phase and quadrature demodulation signals, it needs a high-level field-programmable gate array chip and therefore cannot meet low-power consumption requirements in battery-powered situations. Herein we proposed a novel digital demodulation FHR algorithm using time-domain windowing. Then, we applied the algorithm into a low-power complex programmable logic device to achieve low-cost acquisition of vector velocity and suppress FHR doubling and finally designed a hand-held, noninvasive digital Doppler FHR detection system for continuous perinatal detection. In this work, the comparison curve between the TREND curve generated by the simulator and the measured curve of our FHR detector was obtained. The comparative experiment between the commercial FM-3A FHR detector and our proposed FHR detector was performed, and the relative error and standard error of the obtained FHR data were used to evaluate the two FHR detectors' detection accuracy. The results showed that our FHR detector showed excellent detection accuracy at an accuracy rate of 98%, which was much higher than the average accuracy of the FM-3A FHR detector and indicated our proposed digital FHR detector showed excellent detection performance. It means that the proposed digital windowed FHR demodulation algorithm is practical, the multi-FHR monitoring system is feasible, and our proposed FHR detector has high detection accuracy, effectively suppresses FHR doubling, and meets the requirements of Chinese national standards. It provides an alternative scheme towards the low-cost hand-held digital demodulation FHR detection application.

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“…Similarly, Jezewski et al (2011) proposed an algorithm which varied AC window length according to an adaptive estimate of beat-to-beat intervals. Valderrama et al (2019) developed an open-source AC method that optimizes the peak search parameters using Bayesian optimization. Another approach by Katebi et al (2020) applied unsupervised hidden semi-Markov models to segment the DUS signal for FHR estimation.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures

Vargas-Calixto

Warrick

Kearney

2021

Front. Artif. Intell.

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Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03–0.15 Hz), movement frequency power (MF: 0.15–0.5 Hz), high frequency power (HF: 0.5–1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.

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An open source autocorrelation-based method for fetal heart rate estimation from one-dimensional Doppler ultrasound

Cited by 16 publications

References 38 publications

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

A Novel Ultrasonic Doppler Fetal Heart Rate Detection System Using Windowed Digital Demodulation

Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures

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