Cardiotocography Analysis for Fetal State Classification Using Machine Learning Algorithms

Agrawal, K.C.; Mohan, Harshit

doi:10.1109/iccci.2019.8822218

Cited by 28 publications

(13 citation statements)

References 6 publications

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“…The results show that the Decision Tree gets the best accuracy at 93.3% [16]. On the other hand, Kanika Agrawal et al tried to compare the classification results using Decision Tree, Support Vector Machine, and Naïve Bayes Classifier which were analyzed using R-Studio [17]. The results show that the classification model gets the highest accuracy in the Decision Tree algorithm as same as the research conducted by Subha et al in previous study [16].…”

Section: Introductionmentioning

confidence: 71%

A Cardiotocographic Classification using Feature Selection: A comparative Study

Prasetyo

Prastyo²,

Arti³

2021

j.inf.tech.comp.eng

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Cardiotocography is a series of inspections to determine the health of the fetus in pregnancy. The inspection process is carried out by recording the baby's heart rate information whether in a healthy condition or contrarily. In addition, uterine contractions are also used to determine the health condition of the fetus. Fetal health is classified into 3 conditions namely normal, suspect, and pathological. This paper was performed to compare a classification algorithm for diagnosing the result of the cardiotocographic inspection. An experimental scheme is performed using feature selection and not using it. CFS Subset Evaluation, Info Gain, and Chi-Square are used to select the best feature which correlated to each other. The data set was obtained from the UCI Machine Learning repository available freely. To find out the performance of the classification algorithm, this study uses an evaluation matrix of precision, Recall, F-Measure, MCC, ROC, PRC, and Accuracy. The results showed that all algorithms can provide fairly good classification. However, the combination of the Random Forest algorithm and the Info Gain Feature Selection gives the best results with an accuracy of 93.74%.

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Section: Introductionmentioning

confidence: 71%

A Cardiotocographic Classification using Feature Selection: A comparative Study

Prasetyo

Prastyo²,

Arti³

2021

j.inf.tech.comp.eng

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“…The existing studies [10,13,14,15,19,20,21] generally used all the available features (21) of the CTG dataset to build their models. Mohammad Saber Iraji [8] implemented several neural network models, among them the DSSAEs (deep stacked sparse auto-encoders) achieved the maximum accuracy of 96.77%, yet the model applied all the 21 features.…”

Section: Discussionmentioning

confidence: 99%

“…However, the findings of several research work suggest that the tree-based algorithms handled this situation more effectively and outperformed the other traditional algorithms by reducing the error rate in classification [7,9,10,11]. In many research, the F1-score and sensitivity rate of the Suspicious case varies mostly between 45%-82% and 39%-90% respectively, on the other hand, in Pathological cases, it varies between 64%-97% and 59-97% respectively [13,18,19,20]. Considering clinical fetal health monitoring, it may cause potential risks if those models (low F1-score and sensitivity) are applied in real-life applications.…”

Section: Related Workmentioning

confidence: 99%

Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

Bhowmik¹,

Bhowmik²,

Ali³

et al. 2021

IJITCS

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A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.

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“…However, the sensitivity of FM varies greatly from pregnant women [12], and it is challenging to monitor FM in the long term by subjective judgment. In recent years, wearable health monitoring devices had become a hot spot of research in the biomedical field, and the use of wearable acceleration sensors and modern digital signal processing techniques to achieve automatic recognition of FM has received widespread attention from researchers from all walks of life [13][14][15][16][17][18][19][20][21][22][23][24]. e accelerometers are small, inexpensive, noninvasive, sensitive, and stable and have become the ideal solution for noninvasive FM recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some researchers have used traditional machine learning classifiers to train and predict the extracted raw acceleration time-and frequency-domain feature signals that aim to distinguish the FM signal class of other noisy signal classes [17,[21][22][23][24]. Vullings and Mischi [17] proposed a method of noninvasive monitoring of FM by using TF characteristics.…”

Section: Introductionmentioning

confidence: 99%

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Liang

Peng

2021

Journal of Healthcare Engineering

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Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.

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Cardiotocography Analysis for Fetal State Classification Using Machine Learning Algorithms

Cited by 28 publications

References 6 publications

A Cardiotocographic Classification using Feature Selection: A comparative Study

A Cardiotocographic Classification using Feature Selection: A comparative Study

Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

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