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Fig. 3 | BMC Pediatrics

Fig. 3

From: Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram

Fig. 3

Process of training and classification: 10-second ECGs were first exported from MUSE in XML format for conversion into CSV format within a Python environment. The CSV files were imported into MATLAB to undergo continuous wavelet transform. Oversampling was performed by segmenting the initial ECG segments into fie 2-second intervals. The dataset was then split into a training set (80% of the ECG data) and a test set (the remaining 20%). Model training and 5-fold cross-validation were conducted using three pre-trained architectures: ResNet- 18, InceptionResNet-V2, and NasNetMobile. Performance was evaluated in terms of accuracy, sensitivity, specificity, F1 score, and area under the ROC curve (AUC)

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BMC Pediatrics

ISSN: 1471-2431

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