Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals

Acharya, U. Rajendra; Sudarshan, K; Rong, Soon Qing; Tan, Zechariah; Lim, C.M.; Koh, Joel En Wei; Nayak, S. Dinesh; Bhandary, Sulatha V.

doi:10.1016/j.compbiomed.2017.04.013

Cited by 93 publications

(75 citation statements)

References 48 publications

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“…108/09/09). 171 Materials and methods 172 With the aim to develop a useful and improved automatic method for predicting 173 preterm birth, we followed a general and widely accepted development process [29][30][31][32][33][34][35][36]: 174 1. select or construct a valid batabase for training and testing the model; 175 2. characterize the data and use effective mathematical expressions to formulate the 176 features that reflect their correlation with the target classes;…”

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confidence: 99%

“…Assessing separability, feature selection, and feature ranking 349 In order to estimate the ability of individual features to separate between preterm and 350 term, contraction and dummy intervals, to separate between the entire preterm and term 351 EHG records, and to assess the rank of their ability to classify preterm and term 352 deliveries, we used the two-sample t-test with a pooled variance estimate [45], the 353 Bhattacaryya criterion, i.e., the minimum attainable classification error or Chernoff 354 bound [35,46,47] and the relative entropy criterion, also known as Kullback-Leibler 355 distance or divergence [48]. 356 We dealt with a large number of potential features that could be used for 357 classification.…”

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confidence: 99%

“…between preterm and term, contraction and dummy intervals of the TPEHGT DS, but 1009 also between the entire preterm and term, early and all records of the TPEHG DB. In 1010 comparison to the use of a single frequency band such as 0.34-1.0 Hz [29], or 0.3-3.0 Hz 1011 and MMFE [36], and in comparison to the use of multifrequency band decomposition 1012 approaches such as the EMD method [34,35], or wavelets [35], the proposed method 1013 yielded the classification accuracy of 100% for early records, and quite comparable and 1014 slightly higher classification performance, CA=96.33%, AU C=99.44%, for all records of 1015 the TPEHG DB (Table 11). Sharp cut-offs and high attenuation in the stopbands 1016 appear to be important characteristics to separate frequency bands when the signals 1017 have non-uniform spectral content due to physiological mechanisms residing in separate 1018 frequency bands, as in the case of EHG records.…”

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confidence: 99%

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Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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Predicting preterm birth is uncertain, and numerous scientists are searching for non-invasive methods to improve its predictability. Current researches are based on the analysis of ElectroHysteroGram (EHG) records, which contain information about the electrophysiological properties of the uterine muscle and uterine contractions. Since pregnancy is a long process, we decided to also characterize, for the first time, non-contraction intervals (dummy intervals) of the uterine records, i.e., EHG signals accompanied by a simultaneously recorded external tocogram measuring mechanical uterine activity (TOCO signal). For this purpose, we developed a new set of uterine records, TPEHGT DS, containing preterm and term uterine records of pregnant women, and uterine records of non-pregnant women. We quantitatively characterized contraction intervals (contractions) and dummy intervals of the uterine records of the TPEHGT DS in terms of the normalized power spectra of the EHG and TOCO signals, and developed a new method for predicting preterm birth. The results on the characterization revealed that the peak amplitudes of the normalized power spectra of the EHG and TOCO signals of the contraction and dummy intervals in the frequency band 1.0-2.2 Hz, describing the electrical and mechanical activity of the uterus due to the maternal heart (maternal heart rate), are high only during term pregnancies, when the delivery is still far away; and they are low when the delivery is close. However, these peak amplitudes are also low during preterm pregnancies, when the delivery is still supposed to be far away (thus suggesting the danger of preterm birth); and they are also low or barely present for non-pregnant women. We propose the values of the peak amplitudes of the normalized power spectra due to the influence of the maternal heart, in an electro-mechanical sense, in the frequency band 1.0-2.2 Hz as a new biophysical marker for the preliminary, or early, assessment of the danger of preterm birth. The classification of preterm and term, contraction and dummy intervals of the TPEHGT DS, for the task of the automatic prediction of preterm birth, using sample entropy, the median frequency of the power spectra, and the peak amplitude of the normalized power spectra, revealed that the dummy intervals provide quite comparable and slightly higher classification performances than these features obtained from the contraction intervals. This result suggests a novel and simple clinical technique, not necessarily to seek contraction intervals but using the dummy intervals, for the early assessment of the danger of preterm birth. Using the publicly available TPEHG DB database to predict preterm birth in terms of classifying between preterm and term EHG records, the 1/50 proposed method outperformed all currently existing methods. The achieved classification accuracy was 100% for early records, recorded around the 23rd week of pregnancy; and 96.33%, the area under the curve of 99.44%, for all records of the database. Since the proposed method is cap...

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Characterization and automatic classification of preterm and term uterine records

Jager

Libenšek

Geršak

2018

Preprint

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“…The use of classification methods that take into account unbalanced data such as the weighted extreme learning machine [48] or weighted decision trees [49] could also be explored. In the same context, we should like to point out that we applied SMOTE before splitting up 9 Journal of Sensors the data subsets (training/validation) as has been done in several studies [50][51][52]. It was seen that when performing cross-validation after simple oversampling, the same samples can be included to build the prediction model and evaluate its performance [53].…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, applying SMOTE to such a low minority class would yield samples similar to the original ones and would not solve this limitation. We thus opted to perform SMOTE on the entire database, as has been done in numerous other studies [50][51][52]. We hope to address this limitation in a future work with a larger database.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Labor Induction Success from the Uterine Electrohysterogram

et al. 2019

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Pharmacological agents are often used to induce labor. Failed inductions are associated with unnecessarily long waits and greater maternal-fetal risks, as well as higher costs. No reliable models are currently able to predict the induction outcome from common obstetric data (area under the ROC curve (AUC) between 0.6 and 0.7). The aim of this study was to design an early success-predictor system by extracting temporal, spectral, and complexity parameters from the uterine electromyogram (electrohysterogram (EHG)). Different types of feature sets were used to design and train artificial neural networks: Set_1: obstetrical features, Set_2: EHG features, and Set_3: EHG+obstetrical features. Predictor systems were built to classify three scenarios: (1) induced women who reached active phase of labor (APL) vs. women who did not achieve APL (non-APL), (2) APL and vaginal delivery vs. APL and cesarean section delivery, and (3) vaginal vs. cesarean delivery. For Scenario 3, we also proposed 2-step predictor systems consisting of the cascading predictor systems from Scenarios 1 and 2. EHG features outperformed traditional obstetrical features in all the scenarios. Little improvement was obtained by combining them (Set_3). The results show that the EHG can potentially be used to predict successful labor induction and outperforms the traditional obstetric features. Clinical use of this prediction system would help to improve maternal-fetal well-being and optimize hospital resources.

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A Critical Look at Studies Applying Over-Sampling on the TPEHGDB Dataset

Vandewiele

Dehaene

Janssens

et al. 2019

Artificial Intelligence in Medicine

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Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set.

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Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals

Cited by 93 publications

References 48 publications

Characterization and automatic classification of preterm and term uterine records

Characterization and automatic classification of preterm and term uterine records

Prediction of Labor Induction Success from the Uterine Electrohysterogram

A Critical Look at Studies Applying Over-Sampling on the TPEHGDB Dataset

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