Beyond Heart Murmur Detection: Automatic Murmur Grading from Phonocardiogram

Objective: The objective of this study is to develop an algorithm that can automatically grade the intensity of heart murmurs in pediatric patients from a low-resource rural area. Murmurs are abnormal heart sounds that can provide important information about a patient's clinical condition, and their grading is strongly correlated with the severity of the underlying condition. Methods: The researchers used phonocardiograms (PCGs) recorded from multiple auscultation locations on a large population of pediatric patients. They employed a Mel spectrogram representation of each PCG recording and fed it into an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms. This ensemble was trained to classify each PCG recording based on its murmur grade. To determine the final murmur grade for each patient, a decision rule was proposed that considered all estimated labels from available recordings. The proposed method was cross-validated using a dataset consisting of 3456 PCG recordings from 1007 patients, employing a stratified ten-fold cross-validation approach. Additionally, the method was tested on a hidden test set comprising 1538 PCG recordings from 442 patients. Results: The overall performance of the algorithm in cross-validation for patient-level murmur gradings was evaluated using the unweighted average of sensitivities and F1-scores, which were found to be 86.3% and 81.6%, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs were determined to be 90.7% (93.6%), 75.8% (66.8%), and 92.3% (84.2%), respectively. On the test set, the algorithm achieved an unweighted average sensitivity of 80.4% and an F1-score of 75.8%. Conclusions: This study presents a potential approach for algorithmic pre-screening in low-resource settings where expert screening costs may be high. The proposed method goes beyond the detection of murmurs and provides a characterization of their intensity, which can contribute to enhanced classification of clinical outcomes and improved patient outcomes in these resource constrained environments.. Significance: The development of an algorithm that can automatically grade the intensity of heart murmurs has significant implications for low-resource settings as it can assist healthcare providers in identifying patients who require further evaluation and intervention potentially improving patient outcomes in these resource constrained environments . Objective: This study aims to develop an algorithm that can automatically grade the intensity of heart murmurs in pediatric patients from low resource rural areas . Murmurs are abnormal heart sounds which provide important information about a patient's clinical condition , with their grading being strongly correlated with severity levels . Methods: Phonocardiograms (PCGs) were recorded from multiple locations on pediatric patients . A Mel spectrogram representation was used for each PCG recording , then fed into an ensemble consisting 15 convolutional residual neural networks with channel wise attention mechanisms . An estimation label was assigned to each PCG recording based on its murmur grade , then all estimated labels were taken into consideration when determining final murmur grades per patient . A stratified ten fold cross validation approach was employed using 3456 PCG recordings from 1007 patients , followed by testing on 1538 PCGs from 442 patients . Results: Unweighted average sensitivity & F1 scores during cross validation yielded 86 . 3 % & 81 . 6 % respectively , while sensitivities & F1 scores for absent , soft & loud murmurs were 90 . 7 % & 93 . 6 % ; 75 . 8 % & 66 .

Automatic Grading of Heart Murmurs in Pediatric Patients from Low-Resource Rural Areas

Heart murmurs are abnormal heart sounds that can provide important information about a patient's clinical condition, and their grading is strongly correlated with the severity of the underlying condition. In low-resource rural areas, where expert screening costs may be high, there is a need for an algorithm that can automatically grade the intensity of heart murmurs. This study presents a potential approach to meet this need by developing an algorithm that can accurately classify PCG recordings based on their murmur grade.

Objective

The objective of this study was to develop an algorithm that could automatically grade the intensity of heart murmurs in pediatric patients from low resource rural areas.

Methods

The researchers used phonocardiograms (PCGs) recorded from multiple auscultation locations on a large population of pediatric patients. They employed a Mel spectrogram representation of each PCG recording and fed it into an ensemble of 15 convolutional residual neural networks with channel-wise attention mechanisms. This ensemble was trained to classify each PCG recording based on its murmur grade. To determine the final murmur grade for each patient, a decision rule was proposed that considered all estimated labels from available recordings. The proposed method was cross-validated using a dataset consisting of 3456 PCG recordings from 1007 patients, employing a stratified ten-fold cross-validation approach. Additionally, the method was tested on a hidden test set comprising 1538 PCG recordings from 442 patients.

Results

The overall performance of the algorithm in cross-validation for patient-level murmur gradings was evaluated using the unweighted average of sensitivities and F1-scores, which were found to be 86.3% and 81.6%, respectively. The sensitivities (and F1-scores) for absent, soft, and loud murmurs were determined to be 90.7% (93.6%), 75