论文标题
Heartsiam：心脏声音分类的域不变模型
HeartSiam: A Domain Invariant Model for Heart Sound Classification
论文作者
论文摘要
根据世卫组织，心血管疾病是死亡的主要原因之一。 Phonocartiography（PCG）是一种经济高效的非侵入性方法，适合心脏监测。这项工作的主要目的是将心脏声音分为正常/异常类别。使用不同的听诊器记录心脏声音，从而在域上有所不同。根据最近的研究，这种变异性会影响心脏声音分类。这项工作提出了一种暹罗网络架构，用于学习正常或异常信号与异常信号与正常信号与异常信号之间的差异之间的相似性。通过在所有领域应用这种相似性和差异学习，可以很好地实现域不变的心脏声音分类的任务。我们已经将多域2016 Physionet/CINC挑战数据集用于评估方法。结果：在挑战提供的评估集中，我们的灵敏度为82.8％，特异性为75.3％，平均准确性为79.1％。在克服多域问题的同时，该提出的方法在特异性高达10.9％的特异性方面已经超过了Physionet挑战的第一名方法，并且平均准确性高达5.6％。同样，与类似的最新域不变方法相比，我们的模型收敛速度更快，并且在特异性（4.1％）和平均准确性（1.5％）方面的性能较高，并且学到了相等数量的时期。
Cardiovascular disease is one of the leading causes of death according to WHO. Phonocardiography (PCG) is a costeffective, non-invasive method suitable for heart monitoring. The main aim of this work is to classify heart sounds into normal/abnormal categories. Heart sounds are recorded using different stethoscopes, thus varying in the domain. Based on recent studies, this variability can affect heart sound classification. This work presents a Siamese network architecture for learning the similarity between normal vs. normal or abnormal vs. abnormal signals and the difference between normal vs. abnormal signals. By applying this similarity and difference learning across all domains, the task of domain invariant heart sound classification can be well achieved. We have used the multi-domain 2016 Physionet/CinC challenge dataset for the evaluation method. Results: On the evaluation set provided by the challenge, we have achieved a sensitivity of 82.8%, specificity of 75.3%, and mean accuracy of 79.1%. While overcoming the multi-domain problem, the proposed method has surpassed the first-place method of the Physionet challenge in terms of specificity up to 10.9% and mean accuracy up to 5.6%. Also, compared with similar state-of-the-art domain invariant methods, our model converges faster and performs better in specificity (4.1%) and mean accuracy (1.5%) with an equal number of epochs learned.
