Evaluation of Electrohysterogram Feature Extraction to Classify the Preterm and Term Delivery Groups

Sim, S. S.; Ryou, Hosuk; Kim, HanByul; Han, Jonghee; Park, Kwangsuk

doi:10.1007/978-3-319-02913-9_172

Cited by 11 publications

(18 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, a significant number of studies on the TPEHGDB dataset do not apply any over-sampling technique. However, in these studies, certain decisions concerning the evaluation were often made which raises serious questions concerning the credibility of the provided results [3,26,25,32,8,4]. In many of these studies, results were either not obtained through cross-validation, or crossvalidation was applied on a subset of data subsampled from the original dataset.…”

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

confidence: 99%

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

Vandewiele

Dehaene

Janssens

et al. 2019

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

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

Vandewiele

Dehaene

Janssens

et al. 2019

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

show abstract

“…The features extracted after the prefiltering are root mean square, variance, log detector, mean frequency, median frequency, peak frequency, spectral moment, frequency ratio, approximate entropy, sample entropy, maximal Lyapnov exponent, etc. [3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The classification methods used in conventional methods include the artificial neural network, K-nearest, decision tree, Parzan classifier, etc.…”

Section: Preterm Birth Prediction Via Electrohysterogrammentioning

confidence: 99%

“…SampEn is one of the most widely used and reliable features in the previous studies [8,[11][12][13][14][15]17]. Before feature extraction, a portion of 1.5 minutes of both ends of a signal is removed to remove the bidirectional filtering effects.…”

Section: Feature Extractionmentioning

confidence: 99%

Time-Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Birth

Ryu¹,

Park²

2015

IEIE Transactions on Smart Processing and Computing

View full text Add to dashboard Cite

Abstract:In this paper, a novel method for the classification of term and preterm birth is proposed based on time-frequency analysis of electrohysterogram (EHG) using multivariate empirical mode decomposition (MEMD). EHG is a promising study for preterm birth prediction, because it is lowcost and accurate compared to other preterm birth prediction methods, such as tocodynamometry (TOCO). Previous studies on preterm birth prediction applied prefilterings based on Fourier analysis of an EHG, followed by feature extraction and classification, even though Fourier analysis is suboptimal to biomedical signals, such as EHG, because of its nonlinearity and nonstationarity. Therefore, the proposed method applies prefiltering based on MEMD instead of Fourier-based prefilters before extracting the sample entropy feature and classifying the term and preterm birth groups. For the evaluation, the Physionet term-preterm EHG database was used where the proposed method and Fourier prefiltering-based method were adopted for comparative study. The result showed that the area under curve (AUC) of the receiver operating characteristic (ROC) was increased by 0.0351 when MEMD was used instead of the Fourier-based prefilter.

show abstract

“…Other features-namely, root mean square value, autocorrelation first zero-crossing, contraction, intensity, mean crossing, and normalized range of the signal-are also used. [9][10][11] Frequency domain features include mean frequency, dominant frequency, standard deviation of frequency, median frequency, spectral moments, variance of central frequency, and mean power. 11,12 Recently, timefrequency approaches are used to characterize the EHG signals.…”

Section: Introductionmentioning

confidence: 99%

“…Several works have been done on preterm prediction by using EHG signals. It includes the analysis of the time, 9–11 frequency, 11,12 time–frequency, 13–15 and nonlinear 16–19 domains. The results obtained using these methods have high variance and hence are not used in current clinical practices.…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions

Punitha

Swaminathan

2019

Proc Inst Mech Eng H

View full text Add to dashboard Cite

In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age. 37 weeks) and preterm (gestational age 4 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant (p \ 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.

show abstract

Evaluation of Electrohysterogram Feature Extraction to Classify the Preterm and Term Delivery Groups

Cited by 11 publications

References 0 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

Time-Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Birth

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions

Contact Info

Product

Resources

About