Detection of Heart Murmurs in Phonocardiograms with Parallel Hidden Semi-Markov Models
Andrew McDonald, Mark Gales, Anurag Agarwal"
Abstract
We describe a recurrent neural network and hidden semi Markov model (HSMM) approach to detect heart murmurs in phonocardiogram recordings.This model forms the winning 'CUED Acoustics' entry to the 2022 George B. Moody PhysioNet challenge.Segmentation of the phonocardiogram is a key preprocessing step for many heart sound algorithms.However, most previous work assumes that heart sound recordings only contain S1 and S2 sounds, leading to poorer segmentations of signals that contain a strong murmur.Our approach applies multiple HSMMs, each making different assumptions about a possible murmur, to produce multiple segmentations of the signal.We then compare the confidence of each HSMM's output to produce both a murmur classification and robust segmentation.Evaluated on the hidden test set, our algorithm achieved a weighted accuracy score of 0.776 on the murmur detection task (ranked 2nd of 40 teams, and just 0.004 below the top score).On the clinical outcomes task, the algorithm achieved a challenge cost score of 11144 (ranked 1st of 40 teams).The high performance on both tasks suggests the algorithm is sensitive to clinically significant murmurs.Compared to end-to-end models, the algorithm also provides interpretable results about their location and timing.This makes it a promising tool for symptomatic screening.