  Linear classifiers with random convolution kernels are computationally
efficient methods that need no design or domain knowledge. Unlike deep neural
networks, there is no need to hand-craft a network architecture; the kernels
are randomly generated and only the linear classifier needs training. A
recently proposed method, RandOm Convolutional KErnel Transforms (ROCKETs), has
shown high accuracy across a range of time-series data sets. Here we propose a
multi-scale version of this method, using both high- and low-frequency
components. We apply our methods to inter-burst detection in a cohort of
preterm EEG recorded from 36 neonates <30 weeks gestational age. Two features
from the convolution of 10,000 random kernels are combined using ridge
regression. The proposed multi-scale ROCKET method out-performs the method
without scale: median (interquartile range, IQR) Matthews correlation
coefficient (MCC) of 0.859 (0.815 to 0.874) for multi-scale versus 0.841 (0.807
to 0.865) without scale, p<0.001. The proposed method lags behind an existing
feature-based machine learning method developed with deep domain knowledge, but
is fast to train and can quickly set an initial baseline threshold of
performance for generic and biomedical time-series classification.
