Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care

Idowu, Ibrahim Olatunji; Fergus, Paul; Hussain, Abir Jaafar; Dobbins, Chelsea; Khalaf, Mohammed; Eslava, Raúl V. Casaña; Keight, Robert

doi:10.1109/cit/iucc/dasc/picom.2015.31

Cited by 28 publications

(33 citation statements)

References 28 publications

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“…While impressive (near-perfect) results on the TPEHGDB dataset are reported in many studies [10,20,33,2,16,19,12,27,1,34,21,29,18,17,11,15], these results should be interpreted cautiously as their evaluation methodology is based on applying over-sampling strategies before data partitioning. All these studies apply over-sampling in order to make the distribution of classes more uniform.…”

Section: A Critical Look On Studies Reporting Near-perfect Results Onmentioning

confidence: 90%

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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Section: A Critical Look On Studies Reporting Near-perfect Results Onmentioning

confidence: 90%

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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“…The temporal parameters measuring the intensity of contraction intervals were: signal amplitude, area under contraction curve, and root mean square (RMS) value [ 7 , 8 ]. The spectral parameters estimating shifts and amplitude changes of the power spectrum during pregnancy were: peak, median, or dominant frequency of the power spectrum [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], normalized peak amplitude of the power spectrum [ 5 ], wavelets [ 13 , 20 , 21 , 22 ], and autoregressive coefficients [ 20 , 23 ]. Non-linear parameters estimate regularity, predictability, periodicity, the amount of chaos, and the complexity of a time series.…”

Section: Introductionmentioning

confidence: 99%

Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records

Jager

Geršak

Vouk

et al. 2020

Sensors

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The aim of the present study was to assess the capability of conduction velocity amplitudes and directions of propagation of electrohysterogram (EHG) waves to better distinguish between preterm and term EHG surface records. Using short-time cross-correlation between pairs of bipolar EHG signals (upper and lower, left and right), the conduction velocities and their directions were estimated using preterm and term EHG records of the publicly available Term–Preterm EHG DataSet with Tocogram (TPEHGT DS) and for different frequency bands below and above 1.0 Hz, where contractions and the influence of the maternal heart rate on the uterus, respectively, are expected. No significant or preferred continuous direction of propagation was found in any of the non-contraction (dummy) or contraction intervals; however, on average, a significantly lower percentage of velocity vectors was found in the vertical direction, and significantly higher in the horizontal direction, for preterm dummy intervals above 1.0 Hz. The newly defined features—the percentages of velocities in the vertical and horizontal directions, in combination with the sample entropy of the EHG signal recorded in the vertical direction, obtained from dummy intervals above 1.0 Hz—showed the highest classification accuracy of 86.8% (AUC=90.3%) in distinguishing between preterm and term EHG records of the TPEHGT DS.

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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;…”

mentioning

confidence: 99%

“…A low value of 325 the SE suggests the presence of a physiologic mechanism with periodic behavior, while a 326 high value suggests the absence of a mechanism. The SE and MF have been 327 successfully used to classify individual pregnancy and labor contractions [19][20][21]44], and 328 to classify entire preterm and term EHG records [27,[29][30][31][32][33], which are actually 329 sequences of contraction and non-contraction (dummy) intervals. The MF and PA are 330 suitable features for assessing shifts and intensity of the frequency content in any 331 biological signal and in separate frequency bands.…”

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confidence: 99%

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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Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care

Cited by 28 publications

References 28 publications

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

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

Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records

Characterization and automatic classification of preterm and term uterine records

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