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

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

doi:10.1186/1687-6180-2012-167

Cited by 15 publications

(6 citation statements)

References 38 publications

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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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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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“…Approaches to classifying individual pregnancy and labor contraction bursts incorporated the following features: Root Mean Square (RMS) value [ 6 , 16 ]; amplitude and area under contraction curve [ 16 ]; contraction power [ 16 ]; peak frequency of power spectrum [ 10 , 17 – 19 ]; mean frequency, peak frequency, and median frequency of power spectrum [ 16 , 19 – 21 ]; mean power frequency [ 22 ]; EHG propagation velocity [ 10 , 17 ]; wavelets [ 11 ] [ 20 , 23 , 24 ]; autoregressive (AR) coefficients [ 23 ]; time reversibility [ 20 – 22 ]; sample entropy and Lyapunov exponent [ 19 – 21 ]; variance entropy [ 20 ]; delay vector variance [ 21 ]; approximate entropy [ 22 ]; non-linear correlation coefficient [ 25 ]; and intrinsic mode functions using Empirical Mode Decomposition (EMD) [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…The shift of the spectrum of uterine bursts towards higher frequencies as labor approaches was reported [ 4 , 11 , 12 ]. Therefore, many other methods used a wider frequency band expanding above 1.0 Hz, 0.3-3.0 Hz [ 27 , 30 , 34 – 36 ], 0.1-3.0 Hz [ 20 , 24 , 26 ], 0.3-4.0 Hz [ 27 , 33 ], 0.2-8.0 Hz [ 23 ], 0.05-16.0 Hz [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…To address the problem of the non-stationarity of the EHG signals, and using the frequency band expanding above 1.0 Hz, several multifrequency band decomposition approaches were employed. The wavelet packet transform multiresolution signal decomposition technique, splitting the frequency region 0.0-3.125 Hz of the input signals into eight packets of equal bandwidths, was used to investigate the way the energy distribution of the uterine EHG signal is modified during pregnancy and in labor [ 40 ], and to improve the classification accuracy of complex uterine EHG signals [ 24 ]. The EMD method decomposing the EHG signals into a number of intrinsic mode functions which were sequentially ranked from the high to the low frequency components of the frequency region 0.3-3.0 Hz [ 34 ], or in combination with the wavelet packet decomposition technique [ 35 ], was used to improve the classification accuracy of preterm and term records of the TPEHG DB.…”

Section: Introductionmentioning

confidence: 99%

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Characterization and automatic classification of preterm and term uterine records

2018

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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 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 capable ...

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“…Figure 1 shows the frequency bands of preprocessing bandpass filters used in the previous studies on uterine EMG, which shows there is no globally accepted frequency band for prefiltering. Garfield et al and Lucovnik et al used a frequency band as narrow as 0.34-1 Hz, while many other studies used broader bands like 0.05-1.5 Hz, 0.1-3 Hz, 0.34-3 Hz, 0.08-4 Hz, 0.1-4 Hz, 0.2-8 Hz, and 0.3-50 Hz [7,12,13,[19][20][21]26]. Moreover, different research groups used prefilters of different frequency bands for the analysis of the same database.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor

You

Kim

Seok

et al. 2019

J. Electr. Eng. Technol.

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Non-invasive electrohysterogram (EHG) could be a promising technique for the preterm birth prediction, which could enable us to diagnose the preterm birth before the labor and reduces the infant mortality and morbidity. Previous studies on the preterm birth prediction with EHG have conducted comprehensive researches on various signal features and classification algorithms, but most of them adopted prefilters based on the linear transforms using fixed basis function, although they are suboptimal for the nonlinearity and nonstationarity of the EHG signal. In this paper, multivariate empirical mode decomposition (MEMD) is applied to decompose the electrical activity signal measured on the uterus. After the decomposition, features are calculated for the corresponding oscillations to the uterine contraction. To investigate the performance of the features, three-channel EHG signals of 254 patients (224 term, 30 preterm) are chosen among 300 patients from Physionet term-preterm electrohysterogram (TPEHG) database to extract features from the EHG signals and classify the features using machine learning algorithms. Classification results shows that the proposed method with MEMD achieved 94.66% correctly classified rate (CCR) and 0.987 area under the curve (AUC), which outperformed those with IIR filter implying MEMD provides a new prospect to improve the current preterm birth prediction approach.

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

Cited by 15 publications

References 38 publications

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

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

Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor

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