Chung-Ang University (Kwak et al.)
Model weights not public. Contact creators for more information.
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.
Architecture
Hybrid
Hybrid model combining spectrogram-based deep features with hand-crafted peak-interval (PI) sequence and PI-mean features derived from phonocardiogram peak detection, with an optional demographic-feature branch
Added to catalog
2026-08-12
Phonocardiogram recordings from up to four auscultation locations (aortic, pulmonic, tricuspid, mitral valve areas) collected with a Littmann 3200 digital stethoscope during two pediatric screening campaigns in Paraiba, Brazil; largely pediatric population (neonates to adolescents) plus some pregnant adults, with expert murmur and clinical-outcome annotations.
Three-class classification of heart murmur as present, absent, or unknown from multi-location phonocardiogram recordings, using spectrogram and peak-interval features