August 29, 2022BMC Medical Informatics and Decision Making46 citationsOpen Access

On the analysis of data augmentation methods for spectral imaged based heart sound classification using convolutional neural networks

GZG. Wayne ZhouWashington University in St. Louis YCYunchan ChenCornell University CCCandace ChienChildren's Hospital of Los Angeles

Structured PICO

Population

Cardiac sound recordings (heart sound spectrograms) used for automated classification of normal vs abnormal heart sounds

Intervention

Various data augmentation methods (horizontal flipping, PCA color augmentation, SV perturbations, time/frequency masking, pitch shifting, etc.) applied to a convolutional neural network (CNN)

Comparator

Baseline control CNN model without data augmentation

Outcome

Model performance measured by Precision-Recall Area Under the Curve (PR AUC) for classifying normal vs abnormal heart soundssurrogate

Clinically appropriate data augmentation techniques, such as horizontal flipping of spectrograms, can significantly improve the accuracy of convolutional neural networks in classifying heart sounds.

Abstract

BACKGROUND: The application of machine learning to cardiac auscultation has the potential to improve the accuracy and efficiency of both routine and point-of-care screenings. The use of convolutional neural networks (CNN) on heart sound spectrograms in particular has defined state-of-the-art performance. However, the relative paucity of patient data remains a significant barrier to creating models that can adapt to a wide range of potential variability. To that end, we examined a CNN model's performance on automated heart sound classification, before and after various forms of data augmentation, and aimed to identify the most optimal augmentation methods for cardiac spectrogram analysis. RESULTS: We built a standard CNN model to classify cardiac sound recordings as either normal or abnormal. The baseline control model achieved a PR AUC of 0.763 ± 0.047. Among the single data augmentation techniques explored, horizontal flipping of the spectrogram image improved the model performance the most, with a PR AUC of 0.819 ± 0.044. Principal component analysis color augmentation (PCA) and perturbations of saturation-value (SV) of the hue-saturation-value (HSV) color scale achieved a PR AUC of 0.779 ± 045 and 0.784 ± 0.037, respectively. Time and frequency masking resulted in a PR AUC of 0.772 ± 0.050. Pitch shifting, time stretching and compressing, noise injection, vertical flipping, and applying random color filters negatively impacted model performance. Concatenating the best performing data augmentation technique (horizontal flip) with PCA and SV perturbations improved model performance. CONCLUSION: Data augmentation can improve classification accuracy by expanding and diversifying the dataset, which protects against overfitting to random variance. However, data augmentation is necessarily domain specific. For example, methods like noise injection have found success in other areas of automated sound classification, but in the context of cardiac sound analysis, noise injection can mimic the presence of murmurs and worsen model performance. Thus, care should be taken to ensure clinically appropriate forms of data augmentation to avoid negatively impacting model performance.

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