Aim: The George B. Moody PhysioNet Challenge 2022 raised problems of heart murmur detection and related abnormal cardiac function identification from phonocardiograms (PCGs). This work describes the novel approaches developed by our team, Revenger, to solve these problems. Methods: PCGs were resampled to 1000 Hz, then filtered with a Butterworth band-pass filter of order 3, cutoff frequencies 25 - 400 Hz, and z-score normalized. We used the multi-task learning (MTL) method via hard parameter sharing to train one neural network (NN) model for all the Challenge tasks. We performed neural architecture searching among a set of network backbones, including multi-branch convolutional neural networks (CNNs), SE-ResNets, TResNets, simplified wav2vec2, etc. Based on a stratified splitting of the subjects, 20% of the public data was left out as a validation set for model selection. The AdamW optimizer was adopted, along with the OneCycle scheduler, to optimize the model weights. Results: Our murmur detection classifier received a weighted accuracy score of 0.736 (ranked 14th out of 40 teams) and a Challenge cost score of 12944 (ranked 19th out of 39 teams) on the hidden validation set. Conclusion: We provided a practical solution to the problems of detecting heart murmurs and providing clinical diagnosis suggestions from PCGs.

翻译：目的：George B. Moody PhysioNet 挑战赛 2022 提出了从心音图中检测心脏杂音及识别相关异常心脏功能的问题。本工作描述了我们的团队 Revenger 为解决这些问题而开发的新方法。方法：将心音图重采样至 1000 Hz，然后通过 3 阶巴特沃斯带通滤波器（截止频率 25–400 Hz）进行滤波，并采用 z-score 归一化。我们通过硬参数共享的多任务学习方法训练一个神经网络模型以完成所有挑战任务。我们在一组网络骨干架构中进行了神经架构搜索，包括多分支卷积神经网络、SE-ResNet、TResNet、简化 wav2vec2 等。基于受试者的分层划分，将 20% 的公开数据留作验证集用于模型选择。采用 AdamW 优化器及 OneCycle 调度器优化模型权重。结果：在隐藏验证集上，我们的心脏杂音检测分类器获得了 0.736 的加权准确率（在 40 个参赛队伍中排名第 14）和 12944 的挑战成本得分（在 39 个参赛队伍中排名第 19）。结论：我们提供了一种实用的解决方案，用于从心音图中检测心脏杂音并提供临床诊断建议。