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Granger causal analysis of electrohysterographic and tocographic recordings for classification of term vs. preterm births

Saleem Saqib, Saeed Ahmed, Usman Shabnam, Ferzund Javed, Arshad Jahangir, Mirza Jawad, Manzoor Tareq

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

454--467

EN

According to World Health Organization, 5-18% births around the world are premature, and this rate is on its rise. Recent trend has been to develop computational tools which could support obstetricians in their daily practice. This work is aimed at extracting novel diagnostic features for term vs. preterm births classification based on the dynamics of contraction and non-contractions (dummy) intervals. To achieve this, 26 uterine records of 18 pregnant women, resulting in 13 term and 13 preterm deliveries, were analyzed in this study. Each record was of 30-min duration, and comprised three electrohysterographic (EHG) and one externally recorded tocographic (Toco) signal. Granger causal analysis of contraction and dummy intervals of recorded signals was performed to extract potential features. These include coupling strength and directionality indices, and were subsequently utilized by a quadratic discriminator to classify between term and preterm births. The extracted features achieved high discriminatory power of 94%, 95%, 91%, and 97% for sensitivity, specificity, classification accuracy, and area under the ROC curve, respectively. These findings show that Granger causal analysis might be adopted as a computational tool to quantify EHG-Toco dynamics for the risk-assessment of the preterm delivery.

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Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest

Peng Jin, Hao Dongmei, Yang Lin, Du Mengqing, Song Xiaoxiao, Jiang Hongqing, Zhang Yunhan, Zheng Dingchang

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

352--362

EN

Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26th week of gestation.

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Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing

Lemancewicz A., Borowska M., Kuć P., Jasińska E., Laudański P., Laudański T., Oczeretko E.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

302--307

EN

Prevention and early diagnosis of imminent preterm labor are considered to be the most important perinatal challenge nowadays. Significant progress has been observed on postnatal care of premature infants, but without reducing the prevalence of preterm delivery. Our study was focused on comparison of three methods of spectral analysis of electro-hysterographic (EHG) signals: fast Fourier transform (FFT), wavelet transform (WT) and autoregressive modeling (AR). Complexity of the electrohysterographic signals was analyzed by using: the approximate entropy (ApEn), Lempel–Ziv complexity measure (L–Z). Additionally, the work evaluated the applicability of EHG in diagnosing imminent premature labor. EHG signals were recorded among 60 patients with threatened preterm labor symptoms between the 24th and 34th week of pregnancy. Patients included to the study had a shortened cervix (less than 20 mm) without regular uterine contractions recorded on regular cardiotocography (CTG). The women were divided into two groups: those delivering within 7 days – group A (n = 15) and women delivering after 7 days – group B (n = 45). The study confirmed differences in bioelectrical activity of uterus between patients delivering prematurely within 7 days and after from the EHG registration for all analyzed methods.

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Nieinwazyjna elektrohisterografia w monitorowaniu terapii zagrażającego porodu przedwczesnego

Graczyk S., Stanek R., Opala T., Jezewski M., Łabaj P., Horoba K.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2006

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z. 157

123-129

EN

The paper presents bilogical and clinical background of monitoring of an uterine activity during a pregnancy. The concept of electrohysterography application for tocolysis controlling is also discussed.