Heart sound recordings are a key non-invasive tool to detect both congenital and acquired heart conditions. As part of the George B. Moody PhysioNet Challenge 2022, we present an approach based on Bidirectional Long Short-Term Memory (BiLSTM) neural networks for the detection of murmurs and prediction of clinical outcome from Phonocardiograms (PCGs). We 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, we obtained an average murmur score of 0.652 (with average sensitivities of 0.827 and 0.312 for the Present and Unknown classes and an average specificity of 0.801); and an average outcome score of 12434 (with an average sensitivity of 0.676 and an average specificity of 0.544).