Core Concepts
A novel interpretable and efficient architecture for medical time series processing that achieves performance similar to state-of-the-art deep neural networks with several orders of magnitude fewer parameters.
Abstract
The authors propose a novel architecture called Sparse Mixture of Learned Kernels (SMoLK) for medical time series processing tasks. The key highlights are:
- SMoLK learns a set of lightweight, flexible kernels to construct a single-layer neural network, providing interpretability, efficiency, and robustness.
- The authors introduce novel parameter reduction techniques, such as weight absorption and correlated kernel pruning, to further reduce the size of the network.
- On the task of photoplethysmography (PPG) artifact detection, SMoLK achieves greater than 99% of the performance of the state-of-the-art methods, 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, SMoLK 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.
- The interpretability of SMoLK allows for direct inspection of the learned kernels and their contributions to the output, in contrast to the black-box nature of deep neural networks.
- SMoLK is lightweight enough to be implemented on low-power wearable devices, making it suitable for real-time applications.
Stats
Our largest model has 45.3K parameters, while the state-of-the-art Segade model has 2.3M parameters.
Our medium model has 8.5K parameters, while the Tiny-PPG model has 85.9K parameters.
Our smallest model has 1.4K parameters.