Heart murmurs are abnormal sounds present in heartbeats, caused by turbulent blood flow through the heart. The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome. The recordings are captured from multiple locations around the heart. Our participation investigates the effectiveness of selfsupervised learning for murmur detection. We train the layers of a backbone CNN in a self-supervised way with data from both this year's and the 2016 challenge. We use two different augmentations on each training sample, and normalized temperature-scaled cross-entropy loss. We experiment with different augmentations to learn effective phonocardiogram representations. To build the final detectors we train two classification heads, one for each challenge task. We present evaluation results for all combinations of the available augmentations, and for our multipleaugmentation approach. Our team's, Listen2YourHeart, SSL murmur detection classifier received a weighted accuracy score of 0.737 (ranked 13th out of 40 teams) and an outcome identification challenge cost score of 11946 (ranked 7th out of 39 teams) on the hidden test set.
翻译:心肌素是心跳中出现的异常声音, 由心脏血液流动的动荡造成。 PhysioNet 2022 挑战目标是通过心脏的录音自动检测心脏的音响和自动检测正常与异常临床结果。 记录是从心脏周围多个地点捕捉的。 我们的参与调查了自我监督学习以探测杂音的效果。 我们用来自今年和2016年挑战的数据以自我监督的方式培训骨干CNN的层层。 我们在每个培训样本中使用了两个不同的增量, 以及标准温度尺度的跨血激素损失。 我们实验了不同的增量, 以学习有效的光心电图演示。 为了建立最后的探测器, 我们训练了两个分类头, 每一个挑战任务都是一个。 我们为现有扩音的所有组合和我们的多重提示方法提供了评估结果。 我们的团队, 听力2 Yetheart, SLS 黑素检测分级获得了0. 737的加权精度分数( 在40个团队中排名第13位), 和 结果识别成本评分为 119 。