Key result
Wav2Vec 2.0 classifier achieves ~93% accuracy identifying abnormal heart sounds from synchronized PCG and ECG.
Why the study?
Early CVD pre-screening requires accurate and inexpensive tools, but transformer architectures have seen limited use due to a lack of synchronised PCG and ECG data.
Does a fine-tuned Wav2Vec 2.0-based classifier with an augmented dataset improve the classification of abnormal heart sounds compared to state-of-the-art methods?
Does a fine-tuned Wav2Vec 2.0-based classifier with an augmented dataset improve the classification of abnormal heart sounds compared to state-of-the-art methods?
Fine-tuning a Wav2Vec 2.0 model with an augmented dataset significantly improves the classification accuracy of abnormal heart sounds using PCG and ECG signals, outperforming current state-of-the-art methods.
Cardiovascular diseases (CVDs) are the leading cause of death, resulting in approximately 17.9 million deaths every year. Treatment of CVDs is most effective when detected early, creating a need for accurate and inexpensive pre-screening methods. Recently, deep learning has been explored to classify abnormal heart sounds indicative of CVDs using phonocardiogram (PCG) and electrocardiogram (ECG) signals. However, due to limited synchronised PCG and ECG data, state-of-the-art (SOTA) architectures, such as transformers, have not seen much use within this domain. This work explores the use of traditional signal processing, along with denoising diffusion models WaveGrad and DiffWave, to create an augmented dataset that is used to fine-tune a Wav2Vec 2.0-based classifier. The proposed methods achieve greater than state-of-the-art accuracy, unweighted average recall, sensitivity, specificity and Matthew's correlation coefficient of 93.14% and 92.21%, 93.35%, 90.10% and 0.838, respectively, in the Computing in Cardiology (CinC) 2016 training-a data which consists of synchronised PCG and ECG signals. These methods also achieved 92.98%, 92.48%, 93.63%, 92.48%, and 0.8238 when trained on all CinC databases utilising only PCG signals, which is also greater performance than SOTA methods. These results demonstrate the performance of fine-tuning self-supervised transformer-based models when trained on larger amounts of data, which an augmented dataset can supplement.
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Marocchi et al. (2025) studied Abnormal heart sounds. Wav2Vec 2.0-based classifier with augmented dataset vs. State-of-the-art (SOTA) methods was evaluated on Classification accuracy. A Wav2Vec 2.0-based classifier fine-tuned with an augmented dataset achieved 93.14% accuracy in classifying abnormal heart sounds from synchronized PCG and ECG signals.
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