Comparing uterine electromyography activity of antepartum patients versus term labor patients

Tracking the Changes in Synchrony of the Electrophysiological Activity as the Uterus Approaches Labor Using Magnetomyographic Technique

“…The gap junction function is regulated by structural coupling (the number of gap junctions), functional coupling (permeability), and their degradation. Garfield et al [22][23][24] have shown that gap junctions are sparse throughout pregnancy but increase during delivery in various species. It has been shown that at preterm, although the uterus is spontaneously active, these may be isolated areas of activation due to limited propagation resulting in low-amplitude contractions.…”

“…We based our study on the fact that electromyography (EMG) studies performed on humans by Garfield et al 23,24 show changes in the uterine electrical activity as the uterus approaches labor. Using spectral analysis, EMG studies 25 have shown that the power density of uterine EMG bursts in patients during active labor peaked at 0.71 + 0.05 Hz when compared to nonlaboring term (0.48 + 0.03 Hz) patients.…”

“…Using spectral analysis, EMG studies 25 have shown that the power density of uterine EMG bursts in patients during active labor peaked at 0.71 + 0.05 Hz when compared to nonlaboring term (0.48 + 0.03 Hz) patients. However, none of uterine EMG studies [23][24][25][26] included serial recordings on the patients and provided enough spatial resolution to observe the changes in synchrony of uterine activity as the patient approaches active labor. By recruiting patients close to term, we wanted to track changes in the uterine physiology that results in better propagation and synchronization of electrical burst activity throughout the uterus.…”

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

“…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.…”

“…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.…”