1502900307 utime centar9/12/2023 ![]() ![]() A computer with a Linux operating system installed.For a Full Reproduction of U-Sleep approximately 4 TiB of available storage is needed. *The required hard-disk space depends on the number and sizes of datasets considered. For instance, we trained U-Sleep using 8 CPU cores, 1 GPU and 40 GiB of RAM, please refer to the Full Reproduction of U-Sleep section below. On larger machines, one may benefit from maintaining a larger pool of data loaded in memory. the 8 GiB of RAM suggested above), data must be preprocessed and streamed from disk as demonstrated in the Demo section below. If the considered dataset exceeds the system memory (e.g. Likewise, more resources will speed up training. It is possible to train the model on smaller machines, and without GPUs, but doing so may take considerable time. 1 CUDA enabled GPU (please refer to for a detailed list).Using an already trained U-Sleep model for sleep staging may typically be done on any modern laptop (subject to the software requirements listed below).įor training U-Sleep models from scratch, however, we highly recommend using a Linux based computer with at least the following hardware specifications: You can still use this repository to train the older U-Time model, see U-Time Example below. In the following, we will use the term U-Sleep to denote the resilient high frequency sleep staging model, and U-Time to denote this repository of code used to train and evaluate the U-Sleep model. It builds upon and significantly extends our U-Time repository, published at NeurIPS 2019. This repository stores code for training and evaluating the U-Sleep sleep staging model. U-Time and U-Sleep - What's the Difference? See also this repository for Python bindings to the webserver API. ![]() If you are looking to use our pre-trained U-Sleep model for automated sleep staging, please refer to and follow the displayed guide.on other datasets, you are at the right place! If you are interested to re-implement, extend, or train U-Sleep yourself e.g.In the following we will introduce the software behind U-Sleep in greater detail. It features a command-line interface for initializing, training and evaluating models without needing to modify the underlying codebase. This software allows simultaneous training of U-Sleep across any number of PSG datasets using on-the-fly random selection of input channel configurations. A single instance of the model may be trained to perform accurate and resilient sleep stagingĪcross a wide range of clinical populations and polysomnography (PSG) acquisition protocols. U-Sleep is a fully convolutional deep neural network for automated sleep staging. This document describes the official software package developed for and used to create the free and public sleep staging system U-Sleep. However, the repository has been significantly extended since and may graduallyĭiverge from the version described in. This repository may be used to train both the original U-Time and newer U-Sleep models. The U-Sleep model for resilient high-frequency sleep staging.The U-Time model for general-purpose time-series segmentation. ![]()
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