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Wang M, Li G, Yang Y, Yang Y, Feng Y, Li Y, Liu G, Hao D. Automated analysis of fetal heart rate baseline/acceleration/deceleration using MTU-Net3 + model. Biomed Eng Lett 2024; 14:1037-1048. [PMID: 39220035 PMCID: PMC11362430 DOI: 10.1007/s13534-024-00388-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 09/04/2024] Open
Abstract
In clinical practice, obstetricians use visual interpretation of fetal heart rate (FHR) to diagnose fetal conditions, but inconsistencies among interpretations can hinder accuracy. This study introduces MTU-Net3+, a deep learning model designed for automated, multi-task FHR analysis, aiming to improve diagnostic accuracy and efficiency. The proposed MTU-Net3 + was built upon the UNet3 + architecture, incorporating an encoder, a decoder, full-scale skip connections, and a deep supervision module, and further integrates a self-attention mechanism and bidirectional Long Short-Term Memory layers to enhance its performance. The MTU-Net3 + model accepts the preprocessed 20-minute FHR signals as input, outputting categorical probabilities and baseline values for each time point. The proposed MTU-Net3 + model was trained on a subset of a public database, and was tested on the remaining data of the public database and a private database. In the remaining public datasets, this model achieved F1 scores of 84.21% for deceleration (F1.Dec) and 61.33% for acceleration (F1.Acc), with a Root Mean Square Baseline Difference (RMSD.BL) of 3.46 bpm, 0% of points with an absolute difference exceeding 15 bpm(D15bpm), a Synthetic Inconsistency Coefficient (SI) of 44.82%, and a Morphological Analysis Discordance Index (MADI) of 7.00%. On the private dataset, the model recorded an RMSD.BL of 1.37 bpm, 0% D15bpm, F1.Dec of 100%, F1.Acc of 87.50%, an SI of 12.20% and a MADI of 2.79%. The MTU-Net3 + model proposed in this study performed well in automated FHR analysis, demonstrating its potential as an effective tool in the field of fetal health assessment.
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Affiliation(s)
- Minghan Wang
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 219 Life Sciences Building, 100 Pingleyuan, Beijing, 100124 China
| | - Guangfei Li
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 219 Life Sciences Building, 100 Pingleyuan, Beijing, 100124 China
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, Beijing, China
| | - Yimin Yang
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 219 Life Sciences Building, 100 Pingleyuan, Beijing, 100124 China
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China
| | - Yongxiu Yang
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 219 Life Sciences Building, 100 Pingleyuan, Beijing, 100124 China
| | - Yongkang Feng
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 219 Life Sciences Building, 100 Pingleyuan, Beijing, 100124 China
| | - Yashuang Li
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 219 Life Sciences Building, 100 Pingleyuan, Beijing, 100124 China
| | - Guoli Liu
- Department of Obstetrics, Peking University People’s Hospital, Beijing, China
| | - Dongmei Hao
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 219 Life Sciences Building, 100 Pingleyuan, Beijing, 100124 China
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, Beijing, China
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Bai J, Lu Y, Liu H, He F, Guo X. Editorial: New technologies improve maternal and newborn safety. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1372358. [PMID: 38872737 PMCID: PMC11169838 DOI: 10.3389/fmedt.2024.1372358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/17/2024] [Indexed: 06/15/2024] Open
Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
| | - Huishu Liu
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Fang He
- Department of Obstetrics and Gynecology, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaohui Guo
- Department of Obstetrics, Shenzhen People’s Hospital, Shenzhen, China
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Cao Z, Wang G, Xu L, Li C, Hao Y, Chen Q, Li X, Liu G, Wei H. Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data. Health Inf Sci Syst 2023; 11:16. [PMID: 36950107 PMCID: PMC10025176 DOI: 10.1007/s13755-023-00219-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 02/27/2023] [Indexed: 03/21/2023] Open
Abstract
Purpose Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional computerized cardiotocography (cCTG) approaches have non-negligible calibration errors in feature extraction and heavily rely on the expertise and prior experience to define diagnostic features from CTG or FHR signals. Although previous works have studied deep learning methods for extracting CTG or FHR features, these methods still neglect the clinical information of pregnant women. Methods In this paper, we proposed a multimodal deep learning architecture (MMDLA) for intelligent antepartum fetal monitoring that is capable of performing automatic CTG feature extraction, fusion with clinical data and classification. The multimodal feature fusion was achieved by concatenating high-level CTG features, which were extracted from preprocessed CTG signals via a convolution neural network (CNN) with six convolution layers and five fully connected layers, and the clinical data of pregnant women. Eventually, light gradient boosting machine (LGBM) was implemented as fetal status assessment classifier. The effectiveness of MMDLA was evaluated using a dataset of 16,355 cases, each of which includes FHR signal, UC signal and pertinent clinical data like maternal age and gestational age. Results With an accuracy of 90.77% and an area under the curve (AUC) value of 0.9201, the multimodal features performed admirably. The data imbalance issue was also effectively resolved by the LGBM classifier, with a normal-F1 value of 0.9376 and an abnormal-F1 value of 0.8223. Conclusion In summary, the proposed MMDLA is conducive to the realization of intelligent antepartum fetal monitoring.
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Affiliation(s)
- Zhen Cao
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Guoqiang Wang
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Ling Xu
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Chaowei Li
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
- Nvogene Co., Ltd., Tianjing, China
| | - Yuexing Hao
- Department of Human Centered Design, Cornell University, Ithaca, NY USA
| | - Qinqun Chen
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
| | - Xia Li
- Guangzhou Medical University Second Affiliated Hospital, Guangzhou, China
| | - Guiqing Liu
- The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hang Wei
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
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Bai J, Pan X, Lu Y, Zhong M, Wang H, Zheng Z, Guo X. Comparison of fetal heart rate baseline estimation by the cardiotocograph network and clinicians: a multidatabase retrospective assessment study. Front Cardiovasc Med 2023; 10:1059211. [PMID: 37621563 PMCID: PMC10445644 DOI: 10.3389/fcvm.2023.1059211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 07/21/2023] [Indexed: 08/26/2023] Open
Abstract
Background This study aims to compare the fetal heart rate (FHR) baseline predicted by the cardiotocograph network (CTGNet) with that estimated by clinicians. Material and methods A total of 1,267 FHR recordings acquired with different electrical fetal monitors (EFM) were collected from five datasets: 84 FHR recordings acquired with F15 EFM (Edan, Shenzhen, China) from the Guangzhou Women and Children's Medical Center, 331 FHR recordings acquired with SRF618B5 EFM (Sanrui, Guangzhou, China), 234 FHR recordings acquired with F3 EFM (Lian-Med, Guangzhou, China) from the NanFang Hospital of Southen Medical University, 552 cardiotocographys (CTG) recorded using STAN S21 and S31 (Neoventa Medical, Mölndal, Sweden) and Avalon FM40 and FM50 (Philips Healthcare, Amsterdam, The Netherlands) from the University Hospital in Brno, Czech Republic, and 66 FHR recordings acquired using Avalon FM50 fetal monitor (Philips Healthcare, Amsterdam, The Netherlands) at St Vincent de Paul Hospital (Lille, France). Each FHR baseline was estimated by clinicians and CTGNet, respectively. And agreement between CTGNet and clinicians was evaluated using the kappa statistics, intra-class correlation coefficient, and the limits of agreement. Results The number of differences <3 beats per minute (bpm), 3-5 bpm, 5-10 bpm and ≥10 bpm, is 64.88%, 15.94%, 14.44% and 4.74%, respectively. Kappa statistics and intra-class correlation coefficient are 0.873 and 0.969, respectively. Limits of agreement are -6.81 and 7.48 (mean difference: 0.36 and standard deviation: 3.64). Conclusion An excellent agreement was found between CTGNet and clinicians in the baseline estimation from FHR recordings with different signal loss rates.
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Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
- Auckland Bioengnieering Institute, The University of Auckland, Auckland, New Zeanland
| | - Xiuyu Pan
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Preterm Birth Prevention and Treatment Research Unit, Guangzhou Medical University, Guangzhou, China
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Mei Zhong
- Department of Obstetrics, NanFang Hospital of Southen Medical University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Zheng Zheng
- Department of Obstetrics and Gynecology, Guangzhou Women and Children's Medical Center, Preterm Birth Prevention and Treatment Research Unit, Guangzhou Medical University, Guangzhou, China
| | - Xiaohui Guo
- Department of Obstetrics, Shenzhen People's Hospital, Shenzhen, China
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Bai J, Lu Y, Wang H, Zhao J. How synergy between mechanistic and statistical models is impacting research in atrial fibrillation. Front Physiol 2022; 13:957604. [PMID: 36111152 PMCID: PMC9468674 DOI: 10.3389/fphys.2022.957604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.
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Affiliation(s)
- Jieyun Bai
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Yaosheng Lu
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Information Technology, Jinan University, Guangzhou, China
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Huijin Wang
- College of Information Science and Technology, Jinan University, Guangzhou, China
| | - Jichao Zhao
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings. BIOSENSORS 2022; 12:bios12090691. [PMID: 36140076 PMCID: PMC9496277 DOI: 10.3390/bios12090691] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/19/2022] [Accepted: 08/24/2022] [Indexed: 12/01/2022]
Abstract
We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analysis or as a clinical alert system to assist the practitioner. Three models were developed and used to detect (i) FSs on the MHR channel (the FSMHR model), (ii) the MHR and FSs on the Doppler FHR sensor (the FSDop model), and (iii) FSs on the scalp ECG channel (the FSScalp model). The FSDop model was the most useful because FSs are far more frequent on the Doppler FHR channel. All three models were based on a multilayer, symmetric, GRU, and were trained on data recorded during the first and second stages of delivery. The FSMHR and FSDop models were also trained on antepartum recordings. The training dataset contained 1030 expert-annotated periods (mean duration: 36 min) from 635 recordings. In an initial evaluation of routine clinical practice, 30 fully annotated recordings for each sensor type (mean duration: 5 h for MHR and Doppler sensors, and 3 h for the scalp ECG sensor) were analyzed. The sensitivity, positive predictive value (PPV) and accuracy were respectively 62.20%, 87.1% and 99.90% for the FSMHR model, 93.1%, 95.6% and 99.68% for the FSDop model, and 44.6%, 87.2% and 99.93% for the FSScalp model. We built a second test dataset with a more solid ground truth by selecting 45 periods (lasting 20 min, on average) on which the Doppler FHR and scalp ECG signals were recorded simultaneously. Using scalp ECG data, the experts estimated the true FHR value more reliably and thus annotated the Doppler FHR channel more precisely. The models achieved a sensitivity of 53.3%, a PPV of 62.4%, and an accuracy of 97.29%. In comparison, two experts (blinded to the scalp ECG data) respectively achieved a sensitivity of 15.7%, a PPV of 74.3%, and an accuracy of 96.91% and a sensitivity of 60.7%, a PPV of 83.5% and an accuracy of 98.24%. Hence, the models performed at expert level (better than one expert and worse than the other), although a well-trained expert with good knowledge of FSs could probably do better in some cases. The models and datasets have been included in the Fetal Heart Rate Morphological Analysis open-source MATLAB toolbox and can be used freely for research purposes.
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