A Convolutional Network for Sleep Stages Classification

TL;DR

This paper introduces an ensemble of convolutional neural networks that automatically learns features for sleep stage classification, achieving high accuracy without manual feature engineering.

Contribution

It presents a novel deep learning approach using an ensemble of CNNs to classify sleep stages, avoiding dataset-specific feature engineering.

Findings

Achieved a kappa index of 0.83 on 500 sleep recordings.

Outperformed traditional feature-based methods.

Demonstrated effectiveness of ensemble CNNs in sleep analysis.

Abstract

Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the trained expert can spend several hours scoring a single night recording. Multiple automatic methods have tried to solve these problems in the past, most of them by classifying a feature vector that is engineered for a specific dataset. In this work, we avoid this bias using a deep learning model that learns relevant features without human intervention. Particularly, we propose an ensemble of 5 convolutional networks that achieves a kappa index of 0.83 when classifying a dataset of 500 sleep recordings.