# TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings

Source: [TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings](<https://www.sota2.com/research/paper/68fa68511e209131cea1b6d0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:34:00.401Z

Authors: Taeheon Lee, Jeonghwan Hwang, Honggu Lee

Published: 2020-09-10T01:58:06.000Z

## Abstract

Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are vulnerable to being harmed by irregularities and scarcities in data. In this study, we propose a pre-training technique that handles this challenge in sleep staging tasks. Inspired by conventional methods that experienced physicians have used to classify sleep states from the existence of characteristic waveform shapes, or template patterns, our method introduces a cosine similarity based convolutional neural network to extract representative waveforms from training data. Afterwards, these features guide a model to construct representations based on template patterns. Through extensive experiments, we demonstrated that guiding a neural network with template patterns is an effective approach for sleep staging, since (1) classification performances are significantly enhanced and (2) robustness in several aspects are improved. Last but not least, interpretations on models showed that notable features exploited by trained experts are correctly addressed during prediction in the proposed method.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Time Series Forecasting on SSE Composite Index50 (test)](<https://www.sota2.com/research/sota/time-series-forecasting-on-sse-composite-index50-test.md>): Time Series Forecasting; SSE Composite Index50 (test); MAE: 0.0084.

- [Time series forecasting on SSE Composite Index (test)](<https://www.sota2.com/research/sota/time-series-forecasting-on-sse-composite-index-test.md>): Time Series Forecasting; SSE Composite Index (test); MAE: 0.0138.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2009.05407>)

[PDF](<https://arxiv.org/pdf/2009.05407.pdf>)