Electromechanical coupling of the fetal heart can be evaluated noninvasively using doppler ultrasound (DUS) signal and fetal electrocardiography (fECG). In this study, an efficient model is proposed using K-means clustering and hybrid Support Vector Machine-Hidden Markov Model (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 SVM-HMM 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 beat-to-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.