Clinical assessment of uterine contractions

et al. 2021

Sci Rep

Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs.

Section: Introductionmentioning

confidence: 99%

Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate

Song

Qiao

et al. 2021

Sci Rep

“…Uterine contractions (UCs) are the result of uterine activity in the form of an action potential and are an important clinical indicator in the processes of labour and delivery [1]. UC monitoring is indispensable in the evaluation of the health of both the mother and foetus by obstetricians [2]. Current methods for measuring UCs include the use of intrauterine pressure catheters (IUPCs) and external tocography (TOCO).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram

Computers in Biology and Medicine

Jin

Wang

et al. 2019

Uterine contraction (UC) activity is commonly used to monitor the approach of labour and delivery. Electrohysterograms (EHGs) have recently been used to monitor UC and distinguish between efficient and inefficient contractions. In this study, we aimed to identify UC in EHG signals using a convolutional neural network (CNN). An open-access database (Icelandic 16-electrode EHG database from 45 pregnant women with 122 recordings, DB1) was used to develop a CNN model, and 14000 segments with a length of 45 s (7000 from UCs and 7000 from non-UCs, which were determined with reference to the simultaneously recorded tocography signals) were manually extracted from the 122 EHG recordings. Five-fold cross-validation was applied to evaluate the ability of the CNN to identify UC based on its sensitivity (SE), specificity (SP), accuracy (ACC), and area under the receiver operating characteristic curve (AUC). The CNN model developed using DB1 was then applied to an independent clinical database (DB2) to further test its generalisation for recognizing UCs. The EHG signals in DB2 were recorded from 20 pregnant women using our multi-channel system, and 308 segments (154 from UCs and 154 from non-UCs) were extracted. The CNN model from five-fold cross-validation achieved average SE, SP, ACC, and AUC of 0.87, 0.98, 0.93, and 0.92 for DB1, and 0.88, 0.97, 0.93, and 0.87 for DB2, respectively. In summary, we demonstrated that CNN could effectively identify UCs using EHG signals and could be used as a tool for monitoring maternal and foetal health.

“…Uterine contraction (UC) is the tightening and shortening of the uterine muscles. UC can reflect the progress of pregnancy towards delivery and is a major observation for estimating the approach of delivery [1]. Electrohysterogram (EHG), which reflects uterine electrical activities, is a promising noninvasive technology for external UC monitoring [2].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, four methods have been proposed to assess UCs. Manual palpation, which identifies UC by palpating the parturient abdomen over the uterine corpus, requires the constant bedside presence of a trained operator [1]. Internal uterine catheter (IUPC) is limited by its invasiveness and the need for ruptured membranes [3].…”

Section: Introductionmentioning

confidence: 99%

“…EHG features have been investigated to distinguish between UCs and non-UCs (non-uterine contractions) [6, 7]. These features have been extracted by power spectral density, wavelet packet decomposition [8], autoregression model [9], and other signal processing methods in the time and the frequency domain [1, 7]. Nonlinear processes have also been involved in generating UCs because of the complex interactions between billions of myometrium cells [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Jin

BioMed Research International

Liu

et al. 2019

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.