Dual Bayesian ResNet model for heart murmur detection from multi-location phonocardiogram (PCG) recordings, developed for the George B. Moody PhysioNet Challenge 2022. Each patient's PCG recordings are segmented into overlapping log-mel spectrograms, which are passed through two Bayesian ResNet binary classifiers running simultaneously (present vs. unknown-or-absent, and unknown vs. present-or-absent); the two outputs are aggregated into a patient-level present/unknown/absent murmur classification. An optional second-stage XGBoost model integrates the DBRes output with demographic data and hand-crafted signal features. On the Challenge's official hidden test set, DBRes achieved a weighted accuracy of 0.771 for the murmur-detection task, placing 4th among all teams.

Heart murmur detection model combining spectrogram-derived deep features with hand-crafted peak-interval (PI) features extracted from phonocardiogram recordings, submitted to the George B. Moody PhysioNet Challenge 2022 (team CAU_UMN) and later extended into the 'SpectroHeart' method. Peak-to-peak interval sequences and their summary statistics are combined with spectrogram representations of the PCG signal, optionally alongside patient demographic data, to classify murmur presence across multiple auscultation locations. The team's Challenge submission placed 5th of all teams on the murmur-detection task.