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  • IOS Press  (2)
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  • IOS Press  (2)
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  • 1
    Online Resource
    Online Resource
    IOS Press ; 2022
    In:  Technology and Health Care Vol. 30 ( 2022-02-25), p. 235-242
    In: Technology and Health Care, IOS Press, Vol. 30 ( 2022-02-25), p. 235-242
    Abstract: BACKGROUND: As an essential indicator of labour and delivery, uterine contraction (UC) can be detected by manual palpation, external tocodynamometry and internal uterine pressure catheter. However, these methods are not applicable for long-term monitoring. OBJECTIVE: This paper aims to recognize UCs with electrohysterogram (EHG) and find the best electrode combination with fewer electrodes. METHODS: 112 EHG recordings were collected by our bespoke device in our study. Thirteen features were extracted from EHG segments of UC and non-UC. Four classifiers of the decision tree, support vector machine (SVM), artificial neural network, and convolutional neural network were established to identify UCs. The optimal classifier among them was determined by comparing their classification results. The optimal classifier was applied to evaluate all the possible electrode combinations with one to eight electrodes. RESULTS: The results showed that SVM achieved the best classification capability. With SVM, the combination of electrodes on the right part of the uterine fundus and around the uterine body’s median axis achieved the overall best performance. CONCLUSIONS: The optimal electrode combination with fewer electrodes was confirmed to improve the clinical application for long-term monitoring of UCs.
    Type of Medium: Online Resource
    ISSN: 0928-7329 , 1878-7401
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2022
    detail.hit.zdb_id: 2043772-9
    Location Call Number Limitation Availability
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  • 2
    In: Technology and Health Care, IOS Press, Vol. 31, No. 4 ( 2023-06-30), p. 1105-1117
    Abstract: BACKGROUND: Internet-related technologies have rapidly developed and started to impact the traditional medical practices, which combined wireless communication technology as well as “cloud service” technology with electronic fetal heart monitoring have become a mainstream tendency. OBJECTIVE: To investigate the clinical application value of remote fetal heart rate monitoring mode (RFHRM) on late pregnancy during the coronavirus disease (COVID-19) pandemic. METHODS: From March 2021 to February 2022, we recruited 800 cases of pregnant women received prenatal examination at the Anhui Province Maternity and Child Healthcare Hospital. These pregnant women were randomly divided into two groups: the control group (n= 400), which was given traditional management, and the observation group (n= 400), which received remote monitoring technology on this basis. The two groups were compared with neonatal asphyxia, pregnancy outcomes, Edinburgh postnatal depression scale scores (EPDS), prenatal examination expenses and total time consumption. RESULTS: There were no statistically significant differences between the groups in pregnancy outcome and neonatal outcome (P 〉 0.05). However, total EPDS score of 12.5% pregnant women in the observation group were higher than 12. The TPE group had significantly higher mean EPDS scores compared with the RFHRM group (7.79 ± 3.58 vs 5.10 ± 3.07; P 〈 0.05). The results showed a significant difference in maternity expenses (2949.83 ± 456.07 vs 2455.37 ± 506.67; P 〈 0.05) and total time consumption (42.81 ± 7.60 vs 20.43 ± 4.16; P 〈 0.05) between the groups. CONCLUSION: Remote fetal heart rate monitoring via internet served as an innovative, acceptable, safe and effective reduced-frequency prenatal examination model without affecting the outcome of perinatology of pregnant women with different risk factors.
    Type of Medium: Online Resource
    ISSN: 0928-7329 , 1878-7401
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2023
    detail.hit.zdb_id: 2043772-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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