engleski

Relevance of empirical mode decomposition for fetal heartbeat detection on smartphone devices

Fetal phonocardiography is a re-emerging method for extracting fetal heartbeat signals with a strong potential to be used as an easily accessible system in prenatal monitoring, especially if employed in conjunction with widespread electronic hardware. Since smartphone devices are going through rapid development of their processing power, sensory capabilities and network connectivity, they are becoming a powerful yet underutilized biomedical tool. Within this study we propose novel features for automatic fetal heartbeat detection based on intrinsic mode functions (IMF) gained through empirical mode decomposition. In order to show that more accurate detection can be achieved with IMF- based features added to the conventional set of audio features, we assessed feature relevance and usefulness using ranking and selection techniques. The results suggest that IMF-based features are relevant for the classification task and can improve prediction accuracy by 3.28%.

phonocardiography ; fetal heartbeat ; feature extraction ; feature ranking ; feature selection ; machine learning ; prenatal care

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