使用深度网络进行生理信号的科学发现
Using Deep Networks for Scientific Discovery in Physiological Signals
摘要：
深度神经网络（DNN）在生理信号分类方面取得了显著的成功。在这项研究中，我们提出了一种方法来检查DNN的性能在多大程度上依赖于重新发现信号的现有特征，而不是发现真正的新特征。此外，我们还提供了一种新的方法，从网络的假设空间中“移除”手工设计的特征，从而迫使它尝试学习与已知特征不同的表征，作为科学探索的方法。然后，我们在可解释性领域的现有工作基础上，特别是类激活图，尝试推断网络已经学习了哪些新功能。我们证明了这种方法使用ECG和EEG信号。对于ECG信号，我们表明，对于分类房颤的特定任务，DNN可能会重新发现已知特征。我们还展示了如何使用我们的方法来发现新的功能，通过选择性地删除一些ECG功能和“重新发现”它们。我们进一步研究我们的方法如何被用作检验科学假设的工具。我们模拟这种情况下，眼睛运动的重要性，从脑电图分类睡眠。我们表明，我们的工具可以成功地集中研究人员的注意力，使光模式的数据，否则将被隐藏。
英文摘要：
Deep neural networks (DNN) have shown remarkable success in the classification of physiological signals. In this study we propose a method for examining to what extent does a DNN's performance rely on rediscovering existing features of the signals, as opposed to discovering genuinely new features. Moreover, we offer a novel method of "removing" a hand-engineered feature from the network's hypothesis space, thus forcing it to try and learn representations which are different from known ones, as a method of scientific exploration. We then build on existing work in the field of interpretability, specifically class activation maps, to try and infer what new features the network has learned. We demonstrate this approach using ECG and EEG signals. With respect to ECG signals we show that for the specific task of classifying atrial fibrillation, DNNs are likely rediscovering known features. We also show how our method could be used to discover new features, by selectively removing some ECG features and "rediscovering" them. We further examine how could our method be used as a tool for examining scientific hypotheses. We simulate this scenario by looking into the importance of eye movements in classifying sleep from EEG. We show that our tool can successfully focus a researcher's attention by bringing to light patterns in the data that would be hidden otherwise.
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