Key result
An online approach using a naive Bayes classifier and adaptive Kalman filter dynamically assessed non-invasive fetal ECG signal quality and outperformed alternative methods for fetal heart rate estimation.
A novel online approach using a naive Bayes classifier and adaptive Kalman filter reliably assesses noninvasive fetal ECG signal quality and improves fetal heart rate estimation.
May enable real-time non-invasive fetal monitoring; leaves open prospective clinical validation.
OBJECTIVE: The noninvasive fetal ECG (NI-FECG) from abdominal recordings offers novel prospects for prenatal monitoring. However, NI-FECG signals are corrupted by various nonstationary noise sources, making the processing of abdominal recordings a challenging task. In this paper, we present an online approach that dynamically assess the quality of NI-FECG to improve fetal heart rate (FHR) estimation. METHODS: Using a naive Bayes classifier, state-of-the-art and novel signal quality indices (SQIs), and an existing adaptive Kalman filter, FHR estimation was improved. For the purpose of training and validating the proposed methods, a large annotated private clinical dataset was used. RESULTS: The suggested classification scheme demonstrated an accuracy of Krippendorff's alpha in determining the overall quality of NI-FECG signals. The proposed Kalman filter outperformed alternative methods for FHR estimation achieving accuracy. CONCLUSION: The proposed algorithm was able to reliably reflect changes of signal quality and can be used in improving FHR estimation. SIGNIFICANCE: NI-ECG signal quality estimation and multichannel information fusion are largely unexplored topics. Based on previous works, multichannel FHR estimation is a field that could strongly benefit from such methods. The developed SQI algorithms as well as resulting classifier were made available under a GNU GPL open-source license and contributed to the FECGSYN toolbox.
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Andreotti et al. (2017) studied Fetal heart rate monitoring. Naive Bayes classifier and adaptive Kalman filter for NI-FECG signal quality assessment vs. Alternative methods was evaluated on Fetal heart rate (FHR) estimation accuracy. An online approach using a naive Bayes classifier and adaptive Kalman filter dynamically assessed non-invasive fetal ECG signal quality and outperformed alternative methods for fetal heart rate estimation.
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