Maisam Wahbah, M. S. Zitouni, Raghad Al Sakaji, Kiyoe Funamoto, Namareq Widatalla, Anita Krishnan, Yoshitaka Kimura, A. Khandoker
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The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks.Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework.Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.","PeriodicalId":504973,"journal":{"name":"Frontiers in Physiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep learning framework for noninvasive fetal ECG signal extraction\",\"authors\":\"Maisam Wahbah, M. S. Zitouni, Raghad Al Sakaji, Kiyoe Funamoto, Namareq Widatalla, Anita Krishnan, Yoshitaka Kimura, A. 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引用次数: 0
摘要
介绍:要降低胎儿死亡率,避免胎儿健康出现并发症,就必须掌握主动的健康监测技术。在大流行病、地震和资源匮乏等恶劣环境下,全球许多医疗保健系统无法提供基本服务,尤其是对孕妇而言,这一点至关重要。在这种情况下,能够在医院和家中以直接、快速的方式对胎儿进行持续监测非常重要:方法:通过使用清晰的胎儿心电图(ECG)信号计算重要的生物信号,可以实现对胎儿健康的监测。本研究旨在开发一个框架,直接从 12 个通道的腹部复合信号中检测和识别胎儿心电图的 R 峰。因此,对 70 名无胎儿畸形记录的孕妇(健康和有健康问题)进行了无创信号记录。所提出的模型采用了递归神经网络架构,能稳健地检测出胎儿心电图的 R 峰:为了测试所提出的框架,我们进行了受试者依赖性(5 倍交叉验证)和独立测试(排除一个受试者)。拟议框架的平均准确率分别达到 94.2% 和 88.8%。更具体地说,在绒毛膜层形成的挑战期,"排除一个受试者 "测试的准确率为 86.7%。此外,我们还根据检测到的 R 峰值计算了胎儿的心率,所展示的结果凸显了所提议框架的鲁棒性:这项工作有望满足孕产妇和胎儿医疗保健这一关键行业的需求,并推动相关应用的发展。
A deep learning framework for noninvasive fetal ECG signal extraction
Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions.Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks.Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework.Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.