Key result
A supervised SVM classifier trained with PWD envelope samples and image features achieved an average accuracy of 98% ± 1% for detecting complete cardiac cycles, outperforming other algorithms (p<0.0001).
Why the study?
Automatic detection of complete and measurable cardiac cycles could enable quality assessment and automated analysis of long antenatal pulsed-wave Doppler traces.
p-value: p=< 0.0001
A supervised machine learning approach can accurately detect complete cardiac cycles in fetal pulsed-wave Doppler signals, potentially aiding automated analysis and quality assessment.
May aid automated fetal Doppler analysis; leaves open prospective validation before clinical use.
BACKGROUND AND OBJECTIVE: Pulsed-wave Doppler (PWD) echocardiography is the primary tool for antenatal cardiological diagnosis. Based on it, different measurements and validated reference parameters can be extracted. The automatic detection of complete and measurable cardiac cycles would represent a useful tool for the quality assessment of the PWD trace and the automated analysis of long traces. METHODS: This work proposes and compares three different algorithms for this purpose, based on the preliminary extraction of the PWD velocity spectrum envelopes: template matching, supervised classification over a reduced set of relevant waveshape features, and supervised classification over the whole waveshape potentially representing a cardiac cycle. A custom dataset comprising 43 fetal cardiac PWD traces (174,319 signal segments) acquired on an apical five-chamber window was developed and used for the assessment of the different algorithms. RESULTS: The adoption of a supervised classifier trained with the samples representing the upper and lower envelopes of the PWD, with additional features extracted from the image, achieved significantly better results (p < 0.0001) than the other algorithms, with an average accuracy of 98% ± 1% when using an SVM classifier and a leave-one-subject-out cross-validation. Further, the robustness of the results with respect to the classifier model was proved. CONCLUSIONS: The results reveal excellent detection performance, suggesting that the proposed approach can be adopted for the automatic analysis of long PWD traces or embedded in ultrasound machines as a first step for the extraction of measurements and reference clinical parameters.
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Sulas et al. (2020) studied Fetal cardiac PWD traces (n=43). Supervised SVM classifier trained with PWD envelope samples and image features vs. Template matching and supervised classification over a reduced set of relevant waveshape features was evaluated on Average accuracy of cardiac cycle detection (p=< 0.0001). A supervised SVM classifier trained with PWD envelope samples and image features achieved an average accuracy of 98% ± 1% for detecting complete cardiac cycles, outperforming other algorithms (p<0.0001).
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