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 self-supervised learning for murmur detection. We evaluate the use of a backbone CNN, whose layers are trained 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 multiple-augmentation approach.
翻译:心脏杂音是心跳中出现的异常声音, 是由心脏血液流动动荡引起的。 PhysioNet 2022 挑战目标是从心脏录音中自动检测杂音, 并自动检测正常临床结果。 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有效性。 我们评估了主干CNN的使用情况, 它的层次通过自监督方式接受了来自今年和2016年挑战的数据的培训。 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翼损失。 我们用不同的扩增进行实验, 以学习有效的胸腔图示。 为了建立最后的探测器, 我们训练了两个分类头, 每个任务一个。 我们展示了现有增压组合的所有组合的评估结果, 以及我们多重推荐方法的评估结果 。