  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.
