Key result
A deep learning convolutional neural network model achieved an F-measure of 77.85%, precision of 75.33%, and recall of 80.54% for fetal QRS complex detection from raw non-invasive fetal ECG signals.
Why the study?
Does a deep learning CNN model improve fetal QRS complex detection from raw NI-FECG signals compared to traditional classification methods?
Does a deep learning CNN model improve fetal QRS complex detection from raw NI-FECG signals compared to traditional classification methods?
A deep learning CNN approach can reliably detect fetal QRS complexes from raw non-invasive fetal ECG signals without needing to cancel maternal ECG signals.
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CNN may aid fetal QRS detection in NI-FECG; hypothesis-generating and requires prospective validation before clinical use.
Zhong et al. (2018) studied Fetal QRS complex detection. Convolutional neural network (CNN) model vs. KNN, naive Bayes, and SVM was evaluated on F-measure for fetal QRS complex detection. A deep learning convolutional neural network model achieved an F-measure of 77.85%, precision of 75.33%, and recall of 80.54% for fetal QRS complex detection from raw non-invasive fetal ECG signals.
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