Background: Rapid, reliable, and accurate interpretation of medical signals is crucial for high-stakes clinical decision-making. The advent of deep learning allowed for an explosion of new models that offered unprecedented performance in medical time series processing but at a cost: deep learning models are often compute-intensive and lack interpretability. Methods: We propose Sparse Mixture of Learned Kernels (SMoLK), an interpretable architecture for medical time series processing. The method learns a set of lightweight flexible kernels to construct a single-layer neural network, providing not only interpretability, but also efficiency and robustness. We introduce novel parameter reduction techniques to further reduce the size of our network. We demonstrate the power of our architecture on two important tasks: photoplethysmography (PPG) artifact detection and atrial fibrillation detection from single-lead electrocardiograms (ECGs). Our approach has performance similar to the state-of-the-art deep neural networks with several orders of magnitude fewer parameters, allowing for deep neural network level performance with extremely low-power wearable devices. Results: Our interpretable method achieves greater than 99% of the performance of the state-of-the-art methods on the PPG artifact detection task, and even outperforms the state-of-the-art on a challenging out-of-distribution test set, while using dramatically fewer parameters (2% of the parameters of Segade, and about half of the parameters of Tiny-PPG). On single lead atrial fibrillation detection, our method matches the performance of a 1D-residual convolutional network, at less than 1% the parameter count, while exhibiting considerably better performance in the low-data regime, even when compared to a parameter-matched control deep network.

翻译：背景：快速、可靠且准确的医疗信号解读对于高风险的临床决策至关重要。深度学习技术的出现催生了大量新模型，这些模型在医疗时间序列处理中实现了前所未有的性能，但代价高昂：深度学习模型通常计算密集且缺乏可解释性。方法：我们提出稀疏学得核混合架构（SMoLK），这是一种用于医疗时间序列处理的可解释架构。该方法学习一组轻量级柔性核来构建单层神经网络，不仅提供了可解释性，还兼具高效性和鲁棒性。我们引入了新颖的参数缩减技术以进一步减小网络规模。我们在两个重要任务中展示了该架构的能力：光电容积描记法（PPG）伪影检测和单导联心电图（ECG）的心房颤动检测。我们的方法在参数数量减少数个数量级的情况下，性能与最先进的深度神经网络相当，从而允许在超低功耗可穿戴设备上实现深度神经网络级别的性能。结果：在PPG伪影检测任务中，我们的可解释方法实现了超过最先进方法99%的性能，甚至在具有挑战性的分布外测试集上超越了最先进方法，同时使用的参数显著减少（仅为Segade参数的2%，以及Tiny-PPG参数的大约一半）。在单导联心房颤动检测中，我们的方法以不到1%的参数数量匹配了一维残差卷积网络的性能，同时在低数据场景下表现出显著更优的性能，即使与参数匹配的对照深度网络相比也是如此。