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Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors
Sensors ; 22(9):3352, 2022.
Article in English | ProQuest Central | ID: covidwho-1843240
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.
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Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Sensors Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Sensors Year: 2022 Document Type: Article