Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data.

ABSTRACT:

Purpose

To develop an automated framework for sleep stage scoring from PSG via a deep neural network.

Methods

An automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical center from July 2017 to April 2019. Model performance was evaluated by overall classification accuracy, precision, recall, weighted F1 score, and Cohen's Kappa.

Results

Two hundred ninety-four sleep studies were included in this study; 122 composed the training dataset, 20 composed the validation dataset, and 152 were used in the testing dataset. The network achieved human-level annotation performance with an average accuracy of 0.8181, weighted F1 score of 0.8150, and Cohen's

SUBMITTER: Zhang X 

PROVIDER: S-EPMC7289784 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature