论文标题
listen2 yourheart：一种自我监督的方法，用于检测心跳声中的杂音
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds
论文作者
论文摘要
心脏杂音是心跳中存在异常的声音，这是由于湍流流过心脏而引起的。 Physionet 2022挑战目标是从心脏的音频记录中自动检测杂音，并自动检测正常的临床结果与异常的临床结果。录音是从心脏周围的多个位置捕获的。我们的参与调查了自学学习对杂音检测的有效性。我们以一种自我监督的方式训练骨干CNN的层，并通过今年的挑战和2016年的挑战。我们在每个训练样本上使用两种不同的增强，并使用归一化温度缩放的横向渗透损失。我们尝试不同的增强量来学习有效的声音图表表示。为了构建最终探测器，我们训练两个分类头，一个针对每个挑战任务。我们为所有可用增强的所有组合以及我们的乘法方法提供了评估结果。我们的团队的listy2 yourheart，SSL杂音检测分类器获得了0.737的加权精度得分（在40个团队中排名第13位），结果识别挑战成本成本得分为11946（在39个团队中排名第七）（在39个团队中排名第7）。
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.
