Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (6)

Search Parameters:
Keywords = cardiotocogram

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
4 pages, 2618 KiB  
Interesting Images
Fetus in the Abdominal Cavity After Uterine Rupture in a Primigravida Post-Adenomyosis Enucleation
by Saki Kamata, Hanano Ando, Erina Matsuda, Aiko Aoki, Atsushi Komatsu and Kei Kawana
Diagnostics 2024, 14(22), 2470; https://doi.org/10.3390/diagnostics14222470 - 5 Nov 2024
Viewed by 1014
Abstract
A 35-year-old primigravida with a history of adenomyosis enucleation was diagnosed with abnormal fetal position at 25 weeks of gestation. The patient presented with normal vital signs and no symptoms. A cardiotocogram and transabdominal ultrasound revealed a healthy fetus, normal amniotic fluid volume, [...] Read more.
A 35-year-old primigravida with a history of adenomyosis enucleation was diagnosed with abnormal fetal position at 25 weeks of gestation. The patient presented with normal vital signs and no symptoms. A cardiotocogram and transabdominal ultrasound revealed a healthy fetus, normal amniotic fluid volume, and no intra-abdominal bleeding. Pelvic magnetic resonance imaging (MRI) indicated a ruptured muscular layer of the uterine fundus, with the fetus completely prolapsed into the abdominal cavity. An emergency cesarean section was performed, during which the fetus was found wrapped within the amniotic membrane in the abdominal cavity. The uterus exhibited extensive tearing along the line of the previous surgical scar; however, no hemorrhage was observed. In this case, despite uterine rupture, blood flow through the umbilical cord from the placenta in the uterus resulted in the survival of the fetus. In addition, MRI was essential in determining the appropriate timing to save the fetus. Full article
(This article belongs to the Section Medical Imaging and Theranostics)
Show Figures

Figure 1

22 pages, 2969 KiB  
Article
Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise
by Jayakumar Kaliappan, Apoorva Reddy Bagepalli, Shubh Almal, Rishabh Mishra, Yuh-Chung Hu and Kathiravan Srinivasan
Diagnostics 2023, 13(10), 1692; https://doi.org/10.3390/diagnostics13101692 - 10 May 2023
Cited by 38 | Viewed by 4226
Abstract
Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, [...] Read more.
Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values. Full article
Show Figures

Figure 1

19 pages, 1616 KiB  
Article
Using Machine Learning to Classify Human Fetal Health and Analyze Feature Importance
by Yiqiao Yin and Yash Bingi
BioMedInformatics 2023, 3(2), 280-298; https://doi.org/10.3390/biomedinformatics3020019 - 1 Apr 2023
Cited by 23 | Viewed by 6862
Abstract
The reduction of childhood mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this [...] Read more.
The reduction of childhood mortality is an ongoing struggle and a commonly used factor in determining progress in the medical field. The under-5 mortality number is around 5 million around the world, with many of the deaths being preventable. In light of this issue, cardiotocograms (CTGs) have emerged as a leading tool to determine fetal health. By using ultrasound pulses and reading the responses, CTGs help healthcare professionals assess the overall health of the fetus to determine the risk of child mortality. However, interpreting the results of the CTGs is time consuming and inefficient, especially in underdeveloped areas where an expert obstetrician is hard to come by. Using a support vector machine (SVM) and oversampling, this paper proposes a model that classifies fetal health with an accuracy of 99.59%. To further explain the CTG measurements, an algorithm based off of RISE (Randomized Input Sampling for Explanation of Black-box Models) was created, called Feature Alteration for explanation of Black Box Models (FAB). The findings of this novel algorithm were compared to SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanations (LIME). Overall, this technology allows doctors and medical professionals to classify fetal health with high accuracy and determine which features were most influential in the process. Full article
(This article belongs to the Section Clinical Informatics)
Show Figures

Figure 1

25 pages, 2055 KiB  
Article
Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings
by Samuel Boudet, Agathe Houzé de l’Aulnoit, Laurent Peyrodie, Romain Demailly and Denis Houzé de l’Aulnoit
Biosensors 2022, 12(9), 691; https://doi.org/10.3390/bios12090691 - 27 Aug 2022
Cited by 16 | Viewed by 4561
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Biomedical Signal Processing in Healthcare and Disease Diagnosis)
Show Figures

Graphical abstract

22 pages, 2835 KiB  
Article
A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare
by Andrei Velichko
Sensors 2021, 21(18), 6209; https://doi.org/10.3390/s21186209 - 16 Sep 2021
Cited by 20 | Viewed by 4463
Abstract
Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these [...] Read more.
Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems. Full article
(This article belongs to the Special Issue Data Analytics for Mobile-Health)
Show Figures

Figure 1

12 pages, 474 KiB  
Article
Complexity of Cardiotocographic Signals as A Predictor of Labor
by João Monteiro-Santos, Teresa Henriques, Inês Nunes, Célia Amorim-Costa, João Bernardes and Cristina Costa-Santos
Entropy 2020, 22(1), 104; https://doi.org/10.3390/e22010104 - 16 Jan 2020
Cited by 5 | Viewed by 3588
Abstract
Prediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor [...] Read more.
Prediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor predictors. Linear features retrieved from the SisPorto system for cardiotocogram analysis and nonlinear measures were used to predict labor in a dataset of 1072 antepartum tracings, at between 30 and 35 weeks of gestation. Two groups were defined: Group A—fetuses whose traces date was less than one or two weeks before labor, and Group B—fetuses whose traces date was at least one or two weeks before labor. Results suggest that, compared with linear features such as decelerations and variability indices, compression improves labor prediction both within one (C-Statistics of 0.728) and two weeks (C-Statistics of 0.704). Moreover, the correlation between compression and long-term variability was significantly different in groups A and B, denoting that compression and heart rate variability look at different information associated with whether the fetus is closer to or further from labor onset. Nonlinear measures, compression in particular, may be useful in improving labor prediction as a complement to other fetal heart rate features. Full article
Show Figures

Figure 1

Back to TopTop