Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
Cardiotocography (CTG) is a fetal monitoring technique used to determine the distress level of the fetus during pregnancy and delivery. CTG consists of two different signals including fetal heart rate (FHR) and uterine contraction (UC) activities. The linear features of FHR are the most powerful prognostic indices to ascertain whether the fetus in distress. In addition, it is observed that nonlinear features have produced very great results on the time series analysis in recently. In this context, the classification success of the neural network community designed based on the linear and nonlinear features of FHR is analyzed for the delivery process evaluated in three stages. The experimental results have shown that the system designed to distinguish normal and pathological instances is achieved the best classification accuracy at the first stage of the analysis. Also, the greatest contribution of nonlinear features to the classification accuracy is observed at the second stage of the delivery. Consequently, 92.40%, 83.29%, and 79.22% of accuracy obtained for the three stage of the analysis, respectively.
access_time 28 May 2017 Sunday
Cardiotocography Signals with Artificial Neural Network and Extreme Learning Machine
Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning Repository. The prominent features, such as baseline, the number of acceleration and deceleration patterns, and variability recommended by International Federation of Gynecology and Obstetrics (FIGO) have also taken into account during CTG analysis. The features were applied as the input to feedforward neural network (ANN) and Extreme Learning Machine (ELM) to classify FHR patterns in this study. FHR is recently divided into three classes as normal, suspicious and pathological. According to the results of this study, the accuracy of classification of ANN and ELM were obtained as 91.84% and 93.42%, respectively.
access_time 01 June 2017 Thursday
A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses
Cardiotocography (CTG) is a monitoring technique used routinely during the pregnancy and labor and including the analysis of fetal heart rates and movements with uterine contractions. The fact that CTG signals are interpreted by experts generally with eye and CTG has high false positive rate results in intra- and inter-observer conflicts and causes the observers to frequently notice real pathological cases. Therefore, various computer-aided methods supporting diagnosis process have been developed. In this study, a new approach is suggested based on signal and image processing techniques in order to provide the classification of CTG signals. In particular, morphological, spectral and statistical properties of CTG signals are obtained with the way defined conventionally. A spectrum of the signals containing time-frequency information was transformed into 8-bit gray-level image and it was enabled to build gray level co-occurrence matrix (GLCM). In the final step, a combination of morphological, statistical, spectral and image-based properties was applied as the input to the artificial neural network (ANN). In order to measure the performance of the proposed method, accuracy, sensitivity, specificity and quality indexes were used. The obtained results revealed that image-based features increased the classification success and they gave the best results when they were used with the conventional features.
access_time 03 June 2017 Saturday
Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
access_time 04 July 2017 Tuesday
Using wavelet transform for cardiotocography signals classification
access_time 04 July 2017 Tuesday
Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
access_time 14 October 2017 Saturday
Fetal State Assessment Based on Cardiotocography Parameters Using PCA and AdaBoost
access_time 20 October 2017 Friday
A Study of Artificial Neural Network Training Algorithms for Classification of Cardiotocography Signals
access_time 26 December 2017 Tuesday
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