The presentation entitled “Detection of Heart Sound Murmurs and Clinical Outcome with Bidirectional Long Short-Term Networks”, by Sofia Monteiro (IST DBE MSc Student), Ana Fred and Hugo Plácido da Silva (IST DBE Professors and IT Researchers), have won a Best Oral Presentation Award at the 49th edition of the Computing in Cardiology Conference (CinC).

As part of the George B. Moody PhysioNet Challenge 2022, the authors presented an approach based on Bidirectional Long Short-Term Memory (BiLSTM) neural networks for the detection of murmurs and prediction of clinical outcomes from Phonocardiograms (PCGs). They used the homomorphic, Hilbert, power spectral density, and wavelet envelopes as signal features, from which we extracted fixed-length segments of 4 seconds to train the network.

Using the official challenge scoring metrics, our team SmartBeatIT achieved a murmur weighted accuracy score of 0.751 on the hidden validation set (ranked 8th out of 60 teams), and an outcome cost score of 11222 (ranked 41st out of 60 teams). With 5-fold cross-validation on the training set, it was obtained a murmur score of 0.652 ± 0.043 (with average sensitivities of 0.827 and 0.312 for the Present and Unknown classes and an average specificity of 0.801); and an outcome score of 12434 ± 401 (with an average sensitivity of 0.676 and an average specificity of 0.544).

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