IEEE J Biomed Health Inform - Model based Estimation of Aortic and Mitral valves Opening and Closing Timings in Developing Human Fetuses.

Tópicos

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Resumo

Electromechanical coupling of the fetal heart can be evaluated non-invasively using Doppler Ultrasound (DUS) signal and fetal electrocardiography (fECG). In this study, an efficient model is proposed using K-means clustering and hybrid SVM-HMM modeling techniques. Opening and closing of the cardiac valves were detected from peaks in the high frequency component of the DUS signal decomposed by wavelet analysis. It was previously proposed to automatically identify the valve motion by hybrid Support Vector Machine- Hidden Markov Model (SVM-HMM) [1] based on the amplitude and timing of the peaks. However, in the present study, six patterns were identified for the DUS components which were actually variable on a beat to beat basis and found to be different for the early gestation (16- 32 weeks), compared to the late gestation fetuses (36-41 weeks). The amplitude of the peaks linked to the valve motion was different across the six patterns and this affected the precision of valve motion identification by the previous hybrid SVM-HMM method. Therefore in the present study, clustering of the DUS components based on K-means was proposed and the hybrid SVM-HMM was trained for each cluster separately. The valve motion events were consequently identified more efficiently by beat-to-beat attribution of the DUS component peaks. Applying this method, more than 98.6% of valve motion events were beatto- beat identified with average precision and recall of 83.4% and 84.2% respectively. It was an improvement compared to the hybrid method without clustering with average precision and recall of 79.0% and 79.8%. Therefore, this model would be useful for reliable screening of fetal wellbeing.

Resumo Limpo

electromechan coupl fetal heart can evalu noninvas use doppler ultrasound dus signal fetal electrocardiographi fecg studi effici model propos use kmean cluster hybrid svmhmm model techniqu open close cardiac valv detect peak high frequenc compon dus signal decompos wavelet analysi previous propos automat identifi valv motion hybrid support vector machin hidden markov model svmhmm base amplitud time peak howev present studi six pattern identifi dus compon actual variabl beat beat basi found differ earli gestat week compar late gestat fetus week amplitud peak link valv motion differ across six pattern affect precis valv motion identif previous hybrid svmhmm method therefor present studi cluster dus compon base kmean propos hybrid svmhmm train cluster separ valv motion event consequ identifi effici beattobeat attribut dus compon peak appli method valv motion event beatto beat identifi averag precis recal respect improv compar hybrid method without cluster averag precis recal therefor model use reliabl screen fetal wellb

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