A beneficial effect from music was observed with children during the postoperative period of heart surgery, by means of certain vital signs (heart rate and respiratory rate) and in reduced pain (facial pain scale). Nevertheless, there are gaps to be filled in this area, and studies in greater depth are needed.

Objective: Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments. However, experts are needed to interpret the heart sound recordings, limiting the accessibility of auscultation for cardiac care. The George B. Moody PhysioNet Challenge 2022 invites teams to develop automated approaches for detecting abnormal heart function from multi-location phonocardiogram (PCG) recordings of heart sounds. Approach: For the Challenge, we sourced 5272 PCG recordings from 1568 pediatric patients in rural Brazil. We required the Challenge participants to submit the complete code for training and running their models, improving the transparency, reproducibility, and utility of the diagnostic algorithms. We devised a cost-based evaluation metric that captures the costs of screening, treatment, and diagnostic errors, allowing us to investigate the benefits of algorithmic pre-screening and facilitate the development of more clinically relevant algorithms. Main results: So far, over 80 teams have submitted over 600 algorithms during the course of the Challenge, representing a diversity of approaches in academia and industry. We will update this manuscript to share an analysis of the Challenge after the end of the Challenge. Significance: The use of heart sound recordings for both heart murmur detection and clinical outcome identification allowed us to explore the potential of automated approaches to provide accessible pre-screening of less-resourced populations. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and relevance of the researched conducted during the Challenge.

Objective: To investigate, both objectively and subjectively, the effect of music on children in a pediatric cardiac intensive care unit following heart surgery, in conjunction with standard care.Methods: Randomized clinical trial with placebo, assessing 84 children, aged 1 day to 16 years, during the first 24 hours of the postoperative period, given a 30 minute music therapy session with classical music and observed at the start and end of the session, recording heart rate, blood pressure, mean blood pressure, respiratory rate, temperature and oxygen saturation, plus a facial pain score. Statistical significance was set at 5%.Results: Five of the initial 84 patients (5.9%) refused to participate. The most common type of heart disease was acyanotic congenital with left-right shunt (41% of cases: 44.4% of controls). Statistically significant differences were observed between the two groups after the intervention in the subjective facial pain scale and the objective parameters heart rate and respiratory rate (p < 0.001, p = 0.04 and p = 0.02, respectively).Conclusions: A beneficial effect from music was observed with children during the postoperative period of heart surgery, by means of certain vital signs (heart rate and respiratory rate) and in reduced pain (facial pain scale). Nevertheless, there are gaps to be filled in this area, and studies in greater depth are needed.

The modified BP:eHT13 ratio showed better sensitivity and specificity for the screening of BP abnormalities in children aged 5-12 years.

Auscultation is widely applied in clinical activity, nonetheless sound interpretation is dependent on clinician training and experience. Heart sound features such as spatial loudness, relative amplitude, murmurs, and localization of each component may be indicative of pathology. In this study we propose a segmentation algorithm to extract heart sound components (S1 and S2) based on it's time and frequency characteristics. This algorithm takes advantage of the knowledge of the heart cycle times (systolic and diastolic periods) and of the spectral characteristics of each component, through wavelet analysis. Data collected in a clinical environment, and annotated by a clinician was used to assess algorithm's performance. Heart sound components were correctly identified in 99.5% of the annotated events. S1 and S2 detection rates were 90.9% and 93.3% respectively. The median difference between annotated and detected events was of 33.9 ms.