Rapid, reliable and accurate interpretation of medical time series signals is crucial for high-stakes clinical decision-making. Deep learning methods offered unprecedented performance in medical signal processing but at a cost: they were compute intensive and lacked interpretability. We propose sparse mixture of learned kernels (SMoLK), an interpretable architecture for medical time series processing. SMoLK learns a set of lightweight flexible kernels that form a single-layer sparse neural network, providing not only interpretability but also efficiency, robustness and generalization to unseen data distributions. We introduce parameter reduction techniques to reduce the size of SMoLK networks and maintain performance. We test SMoLK on two important tasks common to many consumer wearables: photoplethysmography artefact detection and atrial fibrillation detection from single-lead electrocardiograms. We find that SMoLK matches the performance of models orders of magnitude larger. It is particularly suited for real-time applications using low-power devices, and its interpretability benefits high-stakes situations.

快速、可靠和准确地解释医疗时间序列信号对于高风险的临床决策至关重要。深度学习方法在医疗信号处理方面提供了前所未有的性能，但代价是：它们是计算密集型的并且缺乏可解释性。我们提出了学习内核的稀疏混合（SMoLK），这是一种用于医疗时间序列处理的可解释架构。 SMoLK 学习一组轻量级灵活的内核，这些内核形成单层稀疏神经网络，不仅提供可解释性，而且还提供对不可见数据分布的效率、鲁棒性和泛化性。我们引入参数缩减技术来减小 SMoLK 网络的大小并保持性能。我们在许多消费者可穿戴设备常见的两项重要任务上测试 SMoLK：光电体积描记法伪影检测和单导联心电图的心房颤动检测。我们发现 SMoLK 与大数量级模型的性能相匹配。它特别适合使用低功耗设备的实时应用，其可解释性有利于高风险情况。