Fetal Distress Classification with Deep Convolutional Neural Network

Harman, Deep Singh; Saini, Munish; Jasdeep, Kaur

doi:10.2174/1573404816999200821162312

Cited by 4 publications

(4 citation statements)

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“…To illustrate the importance of our model, the outcomes of both machine learning ( Comert et al, 2018 ; O’Sullivan et al, 2021 ; Ben Barek et al, 2023 ) and deep learning approaches ( Liu et al, 2021 ; Singh et al, 2021 ) utilizing the identical dataset are presented in Table 3 . Our model exhibits superior performance in terms of ACC, SP, PR, recall, and AUC compared to the aforementioned machine learning methods ( Liu et al, 2021 ; Singh et al, 2021 ). Furthermore, when compared to a specific model ( Liu et al, 2021 ), our model demonstrates notably higher levels of ACC, SP, and recall.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Furthermore, when compared to a specific model ( Liu et al, 2021 ), our model demonstrates notably higher levels of ACC, SP, and recall. It is worth noting that the model discussed ( Singh et al, 2021 ) achieves an ACC of 69.6%, potentially attributed to the limitations of CNNs in capturing temporal features effectively. This observation suggests that our model possesses enhanced classification capabilities.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Umbilical artery pH serves as a recognized marker for fetal acidemia, a condition associated with neonatal complications, such as multiple organ dysfunction in newborns ( Sehdev et al, 1997 ; van den Berg et al, 1996 ). Studies have shown a relationship between FHR and variations in umbilical artery pH ( Singh et al, 2021 ). Consequently, we employed the umbilical artery pH values from the clinical data to classify our dataset into two separate groups in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

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Automatic classification of fetal heart rate based on a multi-scale LSTM network

Rao,

Lu,

et al. 2024

Front. Physiol.

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IntroductionFetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates.MethodsEmploy preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models.ResultsThe results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility.DiscussionCompared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women’s clinical characteristics and disregard the potential impact of varying gestational weeks.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Automatic classification of fetal heart rate based on a multi-scale LSTM network

Rao,

Lu,

et al. 2024

Front. Physiol.

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“…Four umbilical artery pH cutoffs are used to categorize fetuses as acidemic or non-academic: 7.05, 7.10, 7.15, and 7.20 ( Castro et al, 2021 ). The pH value of 7.15 is determined as the threshold value in this paper after extensive research ( Sholapurkar, 2020 ) ( Singh et al, 2021 ). Blood with a pH of less than 7.15 is regarded as hypoxia, whereas blood with a pH of more than 7.15 is considered normal.…”

Section: Methodsmentioning

confidence: 99%

A deep feature fusion network for fetal state assessment

Xiao

Liu

et al. 2022

Front. Physiol.

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CTG (cardiotocography) has consistently been used to diagnose fetal hypoxia. It is susceptible to identifying the average fetal acid-base balance but lacks specificity in recognizing prenatal acidosis and neurological impairment. CTG plays a vital role in intrapartum fetal state assessment, which can prevent severe organ damage if fetal hypoxia is detected earlier. In this paper, we propose a novel deep feature fusion network (DFFN) for fetal state assessment. First, we extract spatial and temporal information from the fetal heart rate (FHR) signal using a multiscale CNN-BiLSTM network, increasing the features’ diversity. Second, the multiscale CNN-BiLSM network and frequently used features are integrated into the deep learning model. The proposed DFFN model combines different features to improve classification accuracy. The multiscale convolutional kernels can identify specific essential information and consider signal’s temporal information. The proposed method achieves 61.97%, 73.82%, and 66.93% of sensitivity, specificity, and quality index, respectively, on the public CTU-UHB database. The proposed method achieves the highest QI on the private database, verifying the proposed method’s effectiveness and generalization. The proposed DFFN combines the advantages of feature engineering and deep learning models and achieves competitive accuracy in fetal state assessment compared with related works.

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