Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

Congenital heart diseases affect about 1% of newborns, representing an important morbidity and mortality factor for several severe conditions, including advanced heart failure [1]. In a 2019 survey, it was estimated that congenital heart diseases affect over 500,000 children in East Africa [2], and about 8 in every 1000 live births [3]. Acquired heart diseases include rheumatic fever and the Kawasaki disease, the former being a serious public health problem in developing regions, e.g., rural Brazil [4]. Several regions of developing countries have difficulties in diagnosing and treating both congenital and acquired heart conditions in children. This is mainly due to the lack of infrastructure and cardiology specialists in geographically large areas and difficulty in accessing health services. In addition, the current COVID-19 pandemic poses new difficulties in the clinical evaluation of patients by delaying important in-person patient-doctor contacts, negatively impacting screening and monitoring activities.

A non-invasive assessment of the mechanical function of the heart, performed at point-of-care settings, can provide early information regarding congenital and acquired heart diseases in children. The lack of early diagnoses of these conditions represents a major public health problem, especially in underprivileged countries with high birth rates [5, 6, 7]. In particular, cardiac auscultation and the analysis of the phonocardiogram (PCG) can unveil fundamental clinical information regarding heart malfunctioning caused by congenital and acquired heart disease in pediatric populations. This is achieved by detecting abnormal sound waves, or heart murmurs, in the PCG signal. Murmurs are abnormal waves generated by turbulent blood flow in cardiac and vascular structures. They are closely associated with specific diseases such as septal defects, failure of ductus arteriosus closure in newborns, and defective cardiac valves.

Succedent the 2016 Challenge, which focused on classifying normal vs. abnormal heart sounds from a single short recording from a single precordial location [8, 9], this year’s Challenge is devoted to detecting the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes.

先天性心脏病影响约1%的新生儿，成为多种严重疾病（包括晚期心力衰竭）的重要发病率和死亡率因素[1]。在2019年的一项调查中，估计东非地区有超过50万儿童患有先天性心脏病[2]，每1000例活产婴儿中约有8例[3]。获得性心脏病包括风湿热和川崎病，前者在发展中国家（如巴西农村）是一个严重的公共卫生问题[4]。发展中国家的一些地区在诊断和治疗儿童先天性及获得性心脏病方面存在困难。这主要归因于地理广阔区域基础设施的缺乏和心脏病专家的短缺，以及获取健康服务的困难。此外，当前的COVID-19大流行在临床评估患者方面带来了新的挑战，由于推迟了重要的面对面医患接触，对筛查和监测活动产生了负面影响。

在诊疗点进行的心脏机械功能的无创评估，可以为儿童先天性及获得性心脏疾病的早期诊断提供相关信息。这些条件的早期诊断不足构成了一个重大的公共卫生问题，尤其是在出生率高的不发达国家[5, 6, 7]。特别是，心脏听诊和心音图（PCG）的分析可以揭示关于先天性心脏病和获得性心脏病在儿科人群中引起的心脏功能紊乱的根本临床信息。这是通过检测PCG信号中的异常声波，或心杂音来实现的。杂音是由心脏和血管结构中湍流血液产生的异常波动。它们与特定的疾病密切相关，如室间隔缺损、新生儿动脉导管未闭的失败以及心脏瓣膜缺陷。

继2016年挑战赛之后，该挑战赛专注于从单个短记录和单个心前部位分类正常与异常的心音[8, 9]，今年的挑战赛致力于从多个听诊位置的多颗心脏音记录中检测杂音的存在或不存在，以及检测临床结果。