Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor

Alamedine, Dima; Khalil, Mohamad; Marque, Catherine

doi:10.1155/2013/485684

Cited by 56 publications

(44 citation statements)

References 23 publications

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“…Meanwhile, Alamedine et al (Alamedine, Khalil, & Marque, 2013) This has been achieved by using an algorithm that generates the Tocography TOCO signal, derived from the EHG, and detects windows with significant changes in amplitude. In order to develop the classifier, a total of eleven spectral, temporal, and nonlinear features were extracted from the EHG signal windows of 12 women, which were classed by experts as being in the first stages of labour.…”

Section: Related Studiesmentioning

confidence: 99%

Advanced artificial neural network classification for detecting preterm births using EHG records

et al. 2016

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Abstract-Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate.

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Section: Related Studiesmentioning

confidence: 99%

Advanced artificial neural network classification for detecting preterm births using EHG records

et al. 2016

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“…Using uterine contractions of the 964 EHG records, and the frequency band of 0.34-1.0 Hz, the reported performance in 965 predicting preterm delivery within seven days (using peak frequency of the power 966 spectrum and propagation velocity), in terms of AU C was 96% [10]. Considering the 967 use of uterine contractions of EHG records, and the frequency band spreading below 968 and above 1.0 Hz, examples of the highest reported performances in classifying between 969 pregnancy and labor contractions in terms of correct classification were 88.72% (using 970 Lyapunov exponent, variance entropy, wavelets related features, the QDA classifier, and 971 133 pregnancy and 133 labor contractions) [20]; in terms of AU C were 85% (using 972 non-linear correlation coefficient, 174/115 pregnancy/labor contractions) [25], 84.2%…”

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confidence: 97%

“…To assess the 433 classification performance of the variety of classification tasks, and for the easier and 434 consistent comparison of the performance results obtained, the same classifier, i.e., the 435 QDA classifier, was used in each case. In comparison to a few other standard classifiers, 436 the QDA classifier already reliably classified between pregnancy and labor 437 contractions [20], and between preterm and term, early and later records of the TPEHG 438 DB [33], therefore the QDA classifier seems suitable for the domain of predicting 439 preterm birth. In the scope of this study, we have also preliminarily tested a few other 440 standard classifiers: naive Bayes, k nearest neighbour, linear discriminant analysis, 441 decision tree, and support vector machine.…”

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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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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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“…Meanwhile, Alamedine et al (Alamedine, Khalil, & Marque, 2013) presented three techniques to identify the most useful features relevant for contraction classification. These included linear features, such as peak frequency, mean frequency and root mean square, and nonlinear features, such as the Lyapunov exponent and sample entropy.…”

Section: Related Studiesmentioning

confidence: 99%

Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction Techniques for Detecting Preterm Births Using EHG Records

Fergus

Idowu

Hussain

et al. 2014

Lecture Notes in Computer Science

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Abstract. Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate.

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Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor

Cited by 56 publications

References 23 publications

Advanced artificial neural network classification for detecting preterm births using EHG records

Advanced artificial neural network classification for detecting preterm births using EHG records

Characterization and automatic classification of preterm and term uterine records

Evaluation of Advanced Artificial Neural Network Classification and Feature Extraction Techniques for Detecting Preterm Births Using EHG Records

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