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Zhao Z, Zhu J, Jiao P, Wang J, Zhang X, Lu X, Zhang Y. Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis. BMC Med Inform Decis Mak 2024; 24:19. [PMID: 38247009 PMCID: PMC10801938 DOI: 10.1186/s12911-024-02423-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. METHODS In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer's convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. RESULTS Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. CONCLUSIONS Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.
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Affiliation(s)
- Zhidong Zhao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China.
| | - Jiawei Zhu
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Pengfei Jiao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
| | - Jinpeng Wang
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
| | - Xiaohong Zhang
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Xinmiao Lu
- College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Yefei Zhang
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China
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Fergus P, Chalmers C, Montanez CC, Reilly D, Lisboa P, Pineles B. Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021. [DOI: 10.1109/tetci.2020.3020061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Zhang Y, Zhao Z, Deng Y, Zhang X, Zhang Y. Reconstruction of missing samples in antepartum and intrapartum FHR measurements via mini-batch-based minimized sparse dictionary learning. IEEE J Biomed Health Inform 2021; 26:276-288. [PMID: 34191734 DOI: 10.1109/jbhi.2021.3093647] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Fetal Heart Rate(FHR), an important recording in Cardiotocography(CTG)-based fetal health status monitoring, is the only information that clinical obstetricians can directly obtain and use. A challenge, however, is that missing samples are very common in FHR due to various causes such as fetal movements and sensor malfunctions. The aim is the development of an inpainting tool which is suitable for different missing lengths q and various total missing percentages Q, as well as for use in online mode. This study focused on two major impediments to existing inpainting methods: the longer the missing length, the more difficult it is to recover with mathematical methods; the reliance on tens of thousands of training samples, and the computational burden caused by full batch-based dictionary learning algorithms. We present a regularized minimization approach to signal recovery, which combines a L0.6-norm minimized sparse dictionary learning algorithm (MSDL) and a model optimization strategy for using a mini-batch version for signal recovery. Using 100 FHR recordings with 2 protocols designed to simulate missing clinical data scenarios, the combined method performed favorably in terms of 5 data analysis metrics and 3 clinical indicators. Comparing 4 inpainting methods, we were able to prove the superiority of the proposed algorithm for both large q and large Q. The experimental results showed the lowest values (2.64 (MAE), 4.68 (RMSE)) when Q=5% with short interval lengths. The developed architecture provides a reference value for the practical application of recovering missing samples online.
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Urdal J, Engan K, Eftestøl T, Haaland SH, Kamala B, Mdoe P, Kidanto H, Ersdal H. Fetal heart rate development during labour. Biomed Eng Online 2021; 20:26. [PMID: 33726745 PMCID: PMC7962212 DOI: 10.1186/s12938-021-00861-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/27/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fresh stillbirths (FSB) and very early neonatal deaths (VEND) are important global challenges with 2.6 million deaths annually. The vast majority of these deaths occur in low- and low-middle income countries. Assessment of the fetal well-being during pregnancy, labour, and birth is normally conducted by monitoring the fetal heart rate (FHR). The heart rate of newborns is reported to increase shortly after birth, but a corresponding trend in how FHR changes just before birth for normal and adverse outcomes has not been studied. In this work, we utilise FHR measurements collected from 3711 labours from a low and low-middle income country to study how the FHR changes towards the end of the labour. The FHR development is also studied in groups defined by the neonatal well-being 24 h after birth. METHODS A signal pre-processing method was applied to identify and remove time periods in the FHR signal where the signal is less trustworthy. We suggest an analysis framework to study the FHR development using the median FHR of all measured heart rates within a 10-min window. The FHR trend is found for labours with a normal outcome, neonates still admitted for observation and perinatal mortality, i.e. FSB and VEND. Finally, we study how the spread of the FHR changes over time during labour. RESULTS When studying all labours, there is a drop in median FHR from 134 beats per minute (bpm) to 119 bpm the last 150 min before birth. The change in FHR was significant ([Formula: see text]) using Wilcoxon signed-rank test. A drop in median FHR as well as an increased spread in FHR is observed for all defined outcome groups in the same interval. CONCLUSION A significant drop in FHR the last 150 min before birth is seen for all neonates with a normal outcome or still admitted to the NCU at 24 h after birth. The observed earlier and larger drop in the perinatal mortality group may indicate that they struggle to endure the physical strain of labour, and that an earlier intervention could potentially save lives. Due to the low amount of data in the perinatal mortality group, a larger dataset is required to validate the drop for this group.
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Affiliation(s)
- Jarle Urdal
- Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway
| | - Kjersti Engan
- Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway
| | - Trygve Eftestøl
- Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway
| | | | - Benjamin Kamala
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
- Department of Obstetrics and Gynecology, Muhimbili National Hospital, Dar es Salaam, Tanzania
| | - Paschal Mdoe
- Haydom Lutheran Hospital, Haydom, Manyara Tanzania
| | - Hussein Kidanto
- School of Medicine, Aga Khan University, Dar es Salaam, Tanzania
| | - Hege Ersdal
- Department of Anesthesiology and Intensive Care, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
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Zhao Z, Deng Y, Zhang Y, Zhang Y, Zhang X, Shao L. DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network. BMC Med Inform Decis Mak 2019; 19:286. [PMID: 31888592 PMCID: PMC6937790 DOI: 10.1186/s12911-019-1007-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 12/16/2019] [Indexed: 11/10/2022] Open
Abstract
Background Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. Methods In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. Results Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively Conclusions Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.
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Affiliation(s)
- Zhidong Zhao
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China. .,Hangdian Smart City Research Center of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China.
| | - Yanjun Deng
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
| | - Yang Zhang
- School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Yefei Zhang
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
| | - Xiaohong Zhang
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
| | - Lihuan Shao
- College of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China
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Fergus P, Hussain A, Al-Jumeily D, Huang DS, Bouguila N. Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms. Biomed Eng Online 2017; 16:89. [PMID: 28679415 PMCID: PMC5498914 DOI: 10.1186/s12938-017-0378-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Accepted: 06/26/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. METHODS This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH < 7.25-foetal deterioration, n = 4; or clinical decision without evidence of pathological outcome measures, n = 24). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. RESULTS The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%. CONCLUSIONS The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies.
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Affiliation(s)
- Paul Fergus
- Applied Computing Research Group, Department of Computer Science, Faculty of Engineering and Technology, Liverpool John Moors University, Byron Street, Liverpool, L3 3AF, UK.
| | - Abir Hussain
- Applied Computing Research Group, Department of Computer Science, Faculty of Engineering and Technology, Liverpool John Moors University, Byron Street, Liverpool, L3 3AF, UK
| | - Dhiya Al-Jumeily
- Applied Computing Research Group, Department of Computer Science, Faculty of Engineering and Technology, Liverpool John Moors University, Byron Street, Liverpool, L3 3AF, UK
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, Tongji University, No. 4800 Caoan Road, Shanghai, 201804, China
| | - Nizar Bouguila
- Concordia Institute for Information Systems Engineering, Concorida University, 1455 de Maisonneuve Blvd West, EV7.632, Montreal, QC, HJ3G 2W1, Canada
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Karvelis P, Spilka J, Georgoulas G, Chudáček V, Stylios CD, Lhotská L. Combining latent class analysis labeling with multiclass approach for fetal heart rate categorization. Physiol Meas 2015; 36:1001-24. [PMID: 25894994 DOI: 10.1088/0967-3334/36/5/1001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The most common approach to assess fetal well-being during delivery is monitoring of fetal heart rate and uterine contractions-the cardiotocogram (CTG). Nevertheless, 40 years since the introduction of CTG to clinical practice, its evaluation is still challenging with high inter- and intra-observer variability. Therefore the development of more objective methods has become an issue of major importance in the field. Unlike the usually proposed approaches to assign classes for classification methods that rely either on biochemical parameters (e.g. pH value) or a simple aggregation of expert judgment, this work investigates the use of an alternative labeling system using latent class analysis (LCA) along with an ordinal classification scheme. The study is performed on a well-documented open-access database, where nine expert obstetricians provided CTG annotations. The LCA is proposed here to produce more objective class labels while the ordinal classification aims to explore the natural ordering, and representation of increased severity, for obtaining the final results. The results are promising suggesting that more effort should be put into this proposed approach.
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Affiliation(s)
- P Karvelis
- Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Greece
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