Cardiotocography (CTG) is a primary tool for real-time monitoring fetal heart rate (FHR) and uterine contraction (UC) signals during labor. Nowadays CTG signal classification models based on deep learning have facilitated end-to-end CTG interpretation through deep neural networks for automatic feature extraction. However, the extreme rarity of abnormal cases in clinical settings has posed great challenges for CTG deep learning. Meanwhile, most Generative Adversarial Network (GAN)-based studies focused on FHR signal generation and ignored the importance of UC signals during labor. Hence, we introduced CTGGAN, a modified GAN specifically designed to simulate both FHR and UC intrapartum signals simultaneously. Our approach incorporated Wasserstein Distance to prevent gradient vanishing during training, alongside a set of latent code vectors that enhance the generator controllability. We also integrated an auxiliary classifier to impose categorical constraints, thereby enriching the diversity of the generated samples. The experimental results demonstrated that CTGGAN effectively generated CTG signals that closely resembled real CTG signals. In addition, the incorporation of CTGGAN significantly improved performance, with a 19.18% increase in sensitivity over the traditional classification without GAN. Furthermore, the comparison with several state-of-the-art CTG classification models underscored the discriminative performance of our CTGGAN. In conclusion, this study effectively enhanced the detection of abnormal cases for intrapartum fetal monitoring.

During intrapartum fetal monitoring, it is significant for early detection and diagnosis of fetal distress. However, the traditional cardiotocography (CTG) interpretation methods heavily rely on physicians’ experience and lack consideration of the clinical features of pregnant women. To overcome these challenges, we propose a multimodal deep learning approach using Convolutional Neural Networks (CNN) and Vision Transformer (ViT) to end-to-end extract detailed and global deep features of CTG signal, respectively, and fuse the clinical features of pregnant women to predict fetal status. The experimental results demonstrate that the combination of CNN and ViT achieves outstanding performance with an average F1 value of 0.74 and an AUC value of 0.84. Furthermore, incorporating clinical features of pregnant women improves the model’s performance with an average F1 value of 0.78 and an area under the curve (AUC) value of 0.87. In summary, the proposed multimodal deep learning model shows the feasibility and effectiveness for intrapartum fetal monitoring.

Late fetal growth restriction (FGR) is a common complication of pregnancy characterized by chronic hypoxia. However, late FGR is in a dilemma of the high incidence but low detection rate. Depending on the non-invasiveness and convenient operation, the routine cardiotocography (CTG) allows continuous monitoring fetal heart rate (FHR) to assess fetal intrauterine stockpiling ability. In this paper, we aimed to explore the FHR pattern of late FGR in routine CTG. For analysis, the FHR features were acquired using routine CTG in a population of 160 healthy and 102 late FGR fetuses published in IEEE Dataport. First, we explored the relationships among FHR features and their importance on late FGR assessment by utilizing hypothesis testing, principal component analysis (PCA) and Spearman correlation analysis. Second, we presented a regression coefficient-based backward-stepwise-selection of association rules analysis (ARA) called backward-stepwise Max-R2Apriori ARA, to find the optimum itemset that helps diagnose late FGRs from healthy fetuses. The hypothesis testing, PCA and Spearman correlation analysis found eight FHR features were highly relevant to the late FGR. Moreover, the backward-stepwise Max-R2 Apriori ARA validated the correlation and interpretation about FHR features of late FGR. In conclusion, the analysis results are consistent with clinical knowledge on late FGR and help screen late FGR in antepartum fetal monitoring.

Cardiotocography (CTG) monitoring is a primary tool to assess the health of the fetus. It is widely used to identify the risk of fetal distress. With the outbreak of big data and artificial intelligence, the use of machine learning to assist obstetricians in CTG interpretation is important to improve diagnostic accuracy and save medical resources. However, imbalanced CTG data brings great challenges to machine learning in intelligent multi-classification. Therefore, we propose a stacked model based on Extreme-Gradient Boosting and Random Forest (XGB-RF) feature selection. Firstly, we use the XGB-RF feature selection method to extract the CTG features of significant influence. Then, we implement the fusion idea of stacking to construct the intelligent model that has a strong anti-interference ability under unbalanced CTG data with selected diverse classifiers. The experimental results showed that compared with existing CTG classification models in the public CTG dataset, the proposed model has further improved the performance and reduced the misjudgment between different classes. According to the results, the accuracy is 96.08%, the F1 score is 93.36%, and area under the ROC curve (AUC) is 0.9883. This indicates that the proposed techniques have a great clinical significance in fetal health monitoring.

The realization of intelligent fetal monitoring is helpful to timely detect fetal abnormality and save medical costs. However, the probability of misjudgment tends to be high when modeling the imbalanced clinical data directly. Moreover, reliable interpretation of cardiotocography (CTG) requires multiple obstetricians to annotate, which consumes excessive time and labor cost. In this paper, a K-means method based on adaptive Genetic Algorithm-Fl balanced diversity Weighted Query-by-Committee (QBC) algorithm (KGA-WQBC) was proposed to solve the above problems. The proposed active learning algorithm was improved from the following two aspects. Firstly, an adaptive KGA operator was designed to conduct a preliminary selection of a large number of unlabeled samples, which increased the accuracy of the committee. Then, the WQBC operator was designed to improve the measurement of the committee's disagreement degree after data exploration. Compared with other initial sample selection strategies and disagreement degree weighting means, the results showed that only 41% of the labeled instances were used to make the evaluation index as high as 98.02%, which had the best overall performance. In conclusion, the proposed KGA-WQBC algorithm is reasonable and feasible, and effectively solves the problem of imbalanced data and reduces the cost of medical personnel labeling for intelligent fetal monitoring.

Antepartum cardiotocography (CTG) monitoring is a crucial screening tool widely utilized to evaluate fetal wellbeing. However, the complexity and non-linearity of CTG usually result in inter-observer and intra-observer variability in a visual CTG interpretation using clinical guidelines. In this paper, a fuzzy C-means clustering based adaptive neuro-fuzzy inference system (FCM-ANFIS) was proposed to automatically classify CTG for antenatal fetal monitoring. Data visualization and spearman correlation analysis were implemented to select CTG features. Then, the fuzzy space was partitioned by using fuzzy Cmeans clustering algorithm, and the adjustment parameters were adjusted through the self-learning mechanism of neural networks and least squares algorithm. The experimental results show that the fuzzy space partition based on FCM clustering could improve the performance of ANFIS, and the proposed FCM-ANFIS model outperforms the state-of-the-art automatic classification of CTG models. In conclusion, the proposed FCM-ANIFIS model has promising learning ability and adaptability for the complexity and uncertainty of antenatal CTG interpretation.

Mutual modes derived from Chromatographic fingerprints of high-quality traditional Chinese medicine (TCM) can provide standards for quality assessment. This paper demonstrates a new method to apply non-negative matrix factorization (NMF) in deriving mutual mode from high performance liquid chromatography (HPLC) fingerprints of TCM. The HPLC fingerprints of the same species of TCM are taken as data set in NMF analysis, from which feature bases containing global features of the original data can be extracted. Reconstruction is performed to utilizing the first feature base and the mutual mode of HPLC fingerprints is obtained by projecting to basis matrix. Satisfactory results have been achieved in the experiment of species identification for Exocarpium Citrus Grandis, which has two primary species, that is, Citius Grandis `Tomentosa' (CGT) and Citius Grandis (L.) Osbeck (CGO). Forty-seven representative batches of CGT collected from GAP in Huazhou (Guangdong Province, China), were served as the original data for mutual mode of CGT. Furthermore, six batches of CGT and six batches of CGO from different places were utilized to test the effectiveness of the mutual mode of CGT. The mutual mode of CGT derived by the proposed NMF-based method can cover the basic information on the whole, since the main representative peaks were contained. Compared with the existing approaches, the similarity results to the proposed mutual mode are more clearly distinguished among different species to some extent. The research indicates that the NMF-based method has a better capability of maintaining and summarizing the data information of original Chromatographic fingerprints and can provide an effective approach to establish the mutual model of HPLC fingerprints of TCM.

Cervical spondylosis (CS) is a disease caused by nonspecific degenerative disorders, therefore it requires high demands for diagnosis due to its diversity and complexity. Patient-reported outcomes (PRO) assessment techniques are on the foundation of psychometrics, which obtain patients' own feelings about daily life, health, subjective satisfaction with treatment and other aspects of personal experience through interviews, questionnaires and other forms of self-assessment. This paper explores the applicability of patient-reported outcomes for clinical evaluation of therapeutic effect of acupuncture for CS and attempts to establish a clinical outcome evaluation model consequently. We introduce SF-36 life quality questionnaire as the main index and visual analogue scale (VAS) as reference index, observe 162 cases as experimental data from a multi-center randomized controlled trial (RCT) on acupuncture for neck pain caused by cervical spondylosis, among others, 150 cases finished the whole course. During the initial phases of study, to verify whether PRO technique is suitable to evaluate the effect of acupuncture for CS, we apply some statistical methods in reliability analysis, validity analysis and responsiveness analysis at different measure times, that is, pre-treatment, post-treatment and during follow-up. The results of reliability analysis show that the Cronbach's statistic alpha of whole scale is 0.834 and that of the standardized items is 0.872. The results of validity analysis shows that 8 common factors selected from SF-36 have an accumulative variance contribution rate of 75.621%, which are represented as physical function (PF), role-physical (RP), general health (GH), mental health (MH), vitality (VT), role-emotion (RE), bodily pain (BP), and society function (SF) respectively. The results of the responsiveness analysis show that except mental health, SF-36 scores at the end of treatment, one month and 3 months after the follow-up differed from those before treatment (P<;0.05). In the further study, we employ three-layer, feed forward neural networks with a back propagation algorithm for the evaluation of acupuncture for CS on the basis of features that were extracted from SF-36. Consequently, the comprehensive assessment model for therapeutic effect of acupuncture for CS based on ANN has good performance with learning precision 96.88%. In general, the experimental results indicate that the PRO techniques have good applicability to the evaluation of therapeutic effect of acupuncture for CS and artificial neural network can offer a feasible approach to the comprehensive assessment as well.