  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 applications facing even modest computational or data
constraints and can further complicate their translation into practice. 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 classification
with significantly reduced parameters and operations. We demonstrate advantages
in Atrial Dysfunction detection including: superior accuracy-per-parameter and
accuracy-per-operation, fast training and inference speeds, and visualization
and interpretation of learned patterns in atrial dysfunction detection on ECG
signals. We also demonstrate impressive performance in seizure prediction using
EEG signals. Our network isolated a few time scales that could be strategically
selected to achieve 90.9% accuracy using only 1,133 active parameters and
consistently converged on pulsatile waveform shapes. This method does not rest
on any constraints or assumptions regarding signal content and could be
leveraged in any area of time series analysis dealing with signals containing
features at many time scales.
