Time Scale Network: An Efficient Shallow Neural Network For Time Series Data in Biomedical Applications.

ABSTRACT: Time series data is often composed of information at multiple time scales, particularly in biomedical data. While numerous deep learning strategies exist to capture this information, many make networks larger, require more data, are more demanding to compute, and are difficult to interpret. This limits their usefulness in real-world settings facing even modest computational or data constraints and can further complicate their translation into real-time processing or edge device applicaitons. We present a minimal, computationally efficient Time Scale Network combining the translation and dilation sequence used in discrete wavelet transforms with traditional convolutional neural networks and back-propagation. The network simultaneously learns features at many time scales for sequence classif

SUBMITTER: Meyer T 

PROVIDER: S-EPMC12074648 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature