A deep neural network approach to heart murmur detection using spectrogram and peak interval features

Congenital heart disease affects about 1% of newborns, posing risks like heart failure and mortality. Developing countries often lack resources for diagnosis and treatment. The George B. Moody PhysioNet Challenge 2022 aims to develop systems for detecting murmurs and clinical outcome events using phonocardiogram (PCG) data, offering a cost-effective method for diagnosing cardiac diseases without invasive procedures. We proposed a deep learning model for this task and achieved the 5th rank out of 40 teams in both Track 1 (murmur detection) and Track 2 (clinical outcome prediction). This paper describes our methods and additional experiments. To extract features from the PCG, we employed techniques including Constant Q Transform (CQT) and Mel-scaled spectrogram (Mel-spectrogram) to generate two-dimensional representations in the frequency–time domain. Additionally, we extracted the Peak Interval (PI) feature, which measures the distance between peaks in the PCG data. This feature is useful because the peak intervals should be shorter in PCG recordings with murmurs. We also considered the sequence and mean of PI as additional features. Our proposed system, titled ‘Phonocardiogram-based Heart murmur Detection using Spectrogram and PI features (SpectroHeart),’ employs the Mel-spectrogram and PI features to detect heart murmurs and assess clinical outcomes. We believe that our deep learning-based system has great potential for automatically detecting heart signals from PCG.