# Random Convolution Kernels with Multi-Scale Decomposition for Preterm EEG Inter-burst Detection

arXiv: https://arxiv.org/abs/2108.02039 · HTML: https://mlsanity.com/paper/2108.02039/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Machine learning without a feature set for detecting bursts in the EEG of preterm infants](https://mlsanity.com/paper/1907.06943/) (2019)

## Built on by (most cited first, 2 of 2)

- [S-Rocket: Selective Random Convolution Kernels for Time Series Classification](https://mlsanity.com/paper/2203.03445/) (2022)
- [Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals](https://mlsanity.com/paper/2308.02425/) (2023)

## Abstract (arXiv)

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.