Pain track analysis during gestation using machine learning techniques (original) (raw)

Detection of fetal distress though a support vector machine based on fetal heart rate parameters

Computers in Cardiology, 2005, 2005

This work aimed at realizing an automatic system for diagnosing fetal sufferance through advanced classification methods applied to reliable indexes extracted from fetal heart rate (FHR) recordings. We selected a set of FHR recordings from a database of 909 exams, which were supplied with the diagnosis at the delivery. The analysis was based on both classical parameters taken from the obstetrical clinical literature and some new indexes already used for HR variability in adults, like the power spectral density (PSD) and the approximate entropy (ApEn). This parameter set was then used as input of a learning machine based on the support vector machine (SVM) algorithm. We obtained a dichotomic classifier, performing the detection of suffering IUGR fetuses from healthy ones. A high percentage of correct classifications, above 84%, was reached by filtering the training set with only 65 of the starting 909 available records.

Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging

IEEE Transactions on Biomedical Engineering, 2000

Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent "pure" facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners.

Recognition of facial expression of fetuses by artificial intelligence (AI)

Journal of Perinatal Medicine, 2021

Objectives The development of the artificial intelligence (AI) classifier to recognize fetal facial expressions that are considered as being related to the brain development of fetuses as a retrospective, non-interventional pilot study. Methods Images of fetal faces with sonography obtained from outpatient pregnant women with a singleton fetus were enrolled in routine conventional practice from 19 to 38 weeks of gestation from January 1, 2020, to September 30, 2020, with completely de-identified data. The images were classified into seven categories, such as eye blinking, mouthing, face without any expression, scowling, smiling, tongue expulsion, and yawning. The category in which the number of fetuses was less than 10 was eliminated before preparation. Next, we created a deep learning AI classifier with the data. Statistical values such as accuracy for the test dataset and the AI confidence score profiles for each category per image for all data were obtained. Results The number of...

A computational model of the infant pain impressions with Gaussian and Nearest Mean Classifier

2013 IEEE International Conference on Control System, Computing and Engineering, 2013

In the last recent years, non-invasive methods through image analysis of facial have been proved to be excellent and reliable tool to diagnose of pain recognition. This paper proposes a new feature vector based Local Binary Pattern (LBP) for the pain detection. Different sampling point and radius weighted are proposed to distinguishing performance of the proposed features. In this work, Infant COPE database is used with illumination added. Multi Scale Retinex (MSR) is applied to remove the shadow. Two different supervised classifiers such as Gaussian and Nearest Mean Classifier are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 90% for Infant COPE database.

Classification of Fetal State using Machine Learning Models

E3S Web of Conferences

In gynecology, the problem of fetus during pregnancy in pregnant women have more interests. In the literature, several means are used to follow the pregnancy such as cardiotocography to measure heart rate, accelerations, fetal movements, and uterine contractions. In this proposed study, we use some algorithms to classify some diseases, and confusion matrix to specify the normal, and suspicious pathology using Random Forest, Support Vector Machine, and Artificial Neural Network. To validate this experimentation, the dataset of UCI has suggested to classify the fetus into three classes: normal, suspicious, and pathological the best performing model for detecting the fetal state is the ANN model which gave better accuracy values for 99.19% for training accuracy and 99.09% for test accuracy.

Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

Jurnal Matematika "MANTIK", 2019

The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy va...

Analysis of Various Signals Acquired from Uterine Contraction to determine true and false labor

Prediction of premature labor is very important factor in this century as the neonatal death ratio is increasing day by day. For the prediction of labor it is necessary to have uterine contraction signals. Analysis of EHG is Consider proper tool for this aim. EHG record the electric activity of uterine muscle. In this paper the signals is downloaded from the Physionet dataset, the work presented in this paper is to determine True and False labour from the Analysis of various signals which is acquired from Uterine Contraction. Linear (mean, median) and non-linear (entropy) feature is extracted from EHG signals and support vector machine (SVM) is applied for classification to get the result whether the labour is term or pre-term.