Novel approach for fetal heart rate classification introducing grammatical evolution

Georgoulas, George; Gavrilis, Dimitris; Tsoulos, Ioannis G.; Stylios, Chrysostomos; Bernardes, João; Groumpos, Peter P.

doi:10.1016/j.bspc.2007.05.003

Cited by 41 publications

(19 citation statements)

References 41 publications

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“…Nonlinear techniques were employed for the analysis of cardiac signals for developing cardiac arrhythmia detection algorithms [6,20,41,57]. The level of oxygenation and blood pressure of fetus was monitored by analysing the Fetal heart rate (FHR) variations, making use of feature extraction, linear and nonlinear correlation and nonlinear classifier methods to discriminate acidemic from normal fetuses [30]. Heart rate and systolic pressure signals were used to assess baroreflex sensitivity (BRS) which is an indicator of increased risk of sudden cardiac death in myocardial infarction patients [13].…”

Section: Introductionmentioning

confidence: 99%

Automated detection of diabetes using higher order spectral features extracted from heart rate signals

Swapna

Acharya

VinithaSree

et al. 2013

IDA

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Diabetes Mellitus, often referred to as diabetes, is a chronic disease that affects a vast majority of world population. The percentage of people affected is increasing every year. Diabetes is very difficult to cure. It can only be kept under control. In this scenario, diagnosis of diabetes is of great importance. In this work, we used Heart Rate Variability (HRV) signals obtained from ECG signals for the purpose of diagnosis of diabetes. We employed signal processing methods to extract features from the HRV signal. Since HRV signals are of nonlinear nature, we made use of Higher Order Spectrum (HOS) based features for analysis. In this paper, we have extracted the HOS features from HRV signals corresponding to normal and diabetic subjects.These selected features were fed independently to seven classifiers namely Gaussian Mixture Model (GMM), Support Vector Machine (SVM), NaïveBayes classifier (NBC), K-Nearest Neighbour (KNN), Probabilistic Neural Network (PNN), Fuzzy classifier and Decision Tree (DT) classifier. The performance of these classifiers was evaluated using accuracy, sensitivity, specificity, positive predictive value, and the area under the receiver operating characteristics curve measures. We observed that the GMM classifier presented the highest accuracy of 90.5%, while the other classifiers presented accuracies in the range of 86.5% to 71.4%. Thus, the proposed Computer Aided Diagnostic (CAD) technique has the ability to detect diabetes efficiently by analyzing the subtle changes in ECG signals that are indicative of the presence of diabetes in a patient. Also, we have proposed unique bispectrum and bicoherence plots for normal and diabetes heart rate signals.

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Section: Introductionmentioning

confidence: 99%

Automated detection of diabetes using higher order spectral features extracted from heart rate signals

Swapna

Acharya

VinithaSree

et al. 2013

IDA

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“…Nevertheless there exists other works suggesting other values 7.10 [12], 7.15 [13]. Considering these facts and on recommendation by obstetricians at the CUNI we used border pH of7.…”

Section: A Input Data Descriptionmentioning

confidence: 98%

Fetal heart rate data pre-processing and annotation

Chudáček

Huptych

Koucký

et al. 2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

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Recording of the cardiotocogram (CTG) consists of fetal heart rate (flIR) and tocographic signal. This method is used routinely for intrapartal evaluation of the well being of the fetus.In this paper we present our approach to preprocessing of the flIR signal. Additionally we present annotation software that was developed in order to obtain new expert annotations of the recordings in our newly established database.

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“…In [14], three different techniques were exploited for feature selection (principal component analysis, group of adaptive models evolution, and neural networks) followed by direct correlation of welldiscriminating feature sets with FHR pathology. Binary particle swarm optimization has also been employed for automated feature selection, followed by classification using support vector machines (SVMs) [15], neural networks, and grammatical evolution [16] techniques.…”

Section: A Backgroundmentioning

confidence: 99%

Fetal Heart Rate Classification Using Generative Models

Dash

Quirk

Djurić

2014

IEEE Trans. Biomed. Eng.

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This paper presents novel methods for classification of fetal heart rate (FHR) signals into categories that are meaningful for clinical implementation. They are based on generative models (GMs) and Bayesian theory. Instead of using scalar features that summarize information obtained from long-duration data, the models allow for explicit use of feature sequences derived from local patterns of FHR evolution. We compare our methods with a deterministic expert system for classification and with a support vector machine approach that relies on system-identification and heart rate variability features. We tested the classifiers on 83 retrospectively collected FHR records, with the gold-standard true diagnosis defined using umbilical cord pH values. We found that our methods consistently performed as well as or better than these, suggesting that the use of GMs and the Bayesian paradigm can bring significant improvement to automatic FHR classification approaches.

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Novel approach for fetal heart rate classification introducing grammatical evolution

Cited by 41 publications

References 41 publications

Automated detection of diabetes using higher order spectral features extracted from heart rate signals

Automated detection of diabetes using higher order spectral features extracted from heart rate signals

Fetal heart rate data pre-processing and annotation

Fetal Heart Rate Classification Using Generative Models

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