Complexity analysis of the uterine electromyography

Moslem, Bassam; Khalil, Mohamad; Marque, Catherine; Diab, Mohamad O.

doi:10.1109/iembs.2010.5626065

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…Previous studies reported that the irregularity of EHG evaluated by SampEn gradually decreases during pregnancy [ 11 ]. Other evidence indicates that the values of SampEn are higher during the latent phase in comparison to the active phase of labor, indicating that the irregularity of the uterine EHG signal is reduced in the active phase of labor [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, previous studies indicate that spontaneous uterine contractions contain nonlinear features [ 9 ]. Additionally, changes in the dynamics of EHG between term low-risk parturient and non-parturient women have rarely been explored by using diverse entropy approaches [ 7 , 10 , 11 ]. Authors highlight the necessity of finding suitable measures that characterize electrohysterographic time series generated by the uterine activity during parturition [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition

Reyes-Lagos

Pliego-Carrillo

Ledesma-Ramírez

et al. 2020

Entropy

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Phase Entropy (PhEn) was recently introduced for evaluating the nonlinear features of physiological time series. PhEn has been demonstrated to be a robust approach in comparison to other entropy-based methods to achieve this goal. In this context, the present study aimed to analyze the nonlinear features of raw electrohysterogram (EHG) time series collected from women at the third trimester of pregnancy (TT) and later during term active parturition (P) by PhEn. We collected 10-min longitudinal transabdominal recordings of 24 low-risk pregnant women at TT (from 35 to 38 weeks of pregnancy) and P (>39 weeks of pregnancy). We computed the second-order difference plots (SODPs) for the TT and P stages, and we evaluated the PhEn by modifying the k value, a coarse-graining parameter. Our results pointed out that PhEn in TT is characterized by a higher likelihood of manifesting nonlinear dynamics compared to the P condition. However, both conditions maintain percentages of nonlinear series higher than 66%. We conclude that the nonlinear features appear to be retained for both stages of pregnancy despite the uterine and cervical reorganization process that occurs in the transition from the third trimester to parturition.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition

Reyes-Lagos

Pliego-Carrillo

Ledesma-Ramírez

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…Also, other sophisticated uterine EMG parameters not considered in this study (e.g. propagation velocity, fractal dimension, complexity [42],. .…”

Section: Discussionmentioning

confidence: 99%

“…The real uterine EMG signals used in this study were recorded on 32 women: 22 women were recorded during pregnancy (33 -41 week of gestation, WG), 7 during labor (37 -42 WG) and 3 during both pregnancy and labor (33)(34)(35)(36)(37)(38)(39)(40)(41)(42). The mean and standard deviations of the gestational ages for pregnancy and labor were 34.14 ± 3.94 and 39.6 ± 1.75 weeks, respectively.…”

Section: Database Descriptionmentioning

confidence: 99%

Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals

Moslem

Diab

Khalil

et al. 2012

EURASIP J. Adv. Signal Process.

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Multisensor data fusion is a powerful solution for solving difficult pattern recognition problems such as the classification of bioelectrical signals. It is the process of combining information from different sensors to provide a more stable and more robust classification decisions. We combine here data fusion with multiresolution analysis based on the wavelet packet transform (WPT) in order to classify real uterine electromyogram (EMG) signals recorded by 16 electrodes. Herein, the data fusion is done at the decision level by using a weighted majority voting (WMV) rule. On the other hand, the WPT is used to achieve significant enhancement in the classification performance of each channel by improving the discrimination power of the selected feature. We show that the proposed approach tested on our recorded data can improve the recognition accuracy in labor prediction and has a competitive and promising performance.

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“…Therefore, nonlinear methods including time reversibility, sample entropy, Lyapunov exponents and delay vector variance [11], nonlinear interdependencies [10], and multifractal analysis [12, 13] are useful for EHG analysis. Some advanced algorithms including the Hilbert transform, cross-correlation [14], correlation coefficient H 2 [5], mutual correlation dimension, cross-approximate entropy [15], and dynamic cumulative sum [16] have also been proposed for UC detection. Besides, classifiers including the support vector machine [17], random forest, and artificial neural network [7] have been developed for automatic UC detection using TOCO, cardiotocogram [18], and EHG signals.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Jin

Hao

Liu

et al. 2019

BioMed Research International

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Background Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.

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Complexity analysis of the uterine electromyography

Cited by 15 publications

References 19 publications

Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition

Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition

Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

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