Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors

Abstract

Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) ≤ 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD ≤ 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.

Assessment of Muscle Coordination Changes Caused by the Use of an Occupational Passive Lumbar Exoskeleton in Laboratory Conditions

“…Multivariate entropy showed high variations when the exoskeleton was not used, but these variations became slight when the exoskeleton was employed, resulting in a very flat slope during the performance of the exercises, showing efforts to maintain the muscular relationship with the exoskeleton. Similar to other studies that have utilized this parameter to characterize muscle pair synchronization (e.g., [19,21,22]), lower values were associated with a higher degree of coupling, and in the present case, this coupling was influenced by the action of the exoskeleton.…”

“…The correlation coefficient can reflect the linear correlation between two signals in the time domain. It has been used in the literature to characterize pairs of uterine muscle synchronizations before labor [ 19 , 21 ], with a significant increase in its value as the delivery approached. Furthermore, King [ 22 ] used it to find correlations during movement compared to static poses, also obtaining higher values in dynamic versus static conditions.…”

“…De Marchis et al [ 23 ] calculated this variable to study the intermuscular synchronization in a free pedaling task, finding peak values of coherence in the soleous and gastrocneminus medialis pair of muscles. Coherence was also assessed for labor prediction in [ 21 ], with greater values obtained as delivery approached. Coherence was computed to study post-stroke muscle interactions in the deltoid and triceps muscles in [ 24 ], and the values of coherence were lower for patients than for the control.…”

“…The mutual information coefficient (MI) is a general method for detecting linear and nonlinear statistical dependencies between time series. This variable was also assessed in the aforementioned work on uterine muscle synchronizations [ 19 , 21 ], with higher values of MI as the delivery advanced. Furthermore, the authors of Wu et al [ 25 ] proposed a methodology based on MI to analyze intermuscular coupling during the movement of the upper limbs, obtaining higher values for the triceps brachii and posterior deltoid pair compared to static states.…”

“…It provides a robust relative complexity measurement for multivariate data and has been validated on real-world multivariate gait, physiological, and wind data [ 28 ]. It has also been used to study uterine muscles during delivery progression [ 21 ], demonstrating very low values with the approach of delivery. Muscle interactions between dynamic and resting states were also addressed by MSE computation in [ 22 ], finding lower values and therefore stronger couplings in the external oblique and transverse pair of muscles.…”