SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography

• URL: http://arxiv.org/abs/2404.06869v1
• Date: Wed, 10 Apr 2024 09:47:34 GMT
• Title: SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography
• Authors: Shirel Attia, Revital Shani Hershkovich, Alissa Tabakhov, Angeleene Ang, Sharon Haimov, Riva Tauman, Joachim A. Behar,
• Abstract summary: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Recent data-driven algorithms for sleep staging have shown high performance on local test sets but lower performance on external datasets. Sleep-Net2 sets a new standard for staging sleep from raw PPG time-series.
• Score: 0.7927502566022343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Traditionally, this analysis is conducted in clinical settings and involves a time-consuming scoring procedure. Recent data-driven algorithms for sleep staging, using the photoplethysmogram (PPG) time series, have shown high performance on local test sets but lower performance on external datasets due to data drift. Methods: This study aimed to develop a generalizable deep learning model for the task of four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from raw PPG physiological time-series. Six sleep datasets, totaling 2,574 patients recordings, were used. In order to create a more generalizable representation, we developed and evaluated a deep learning model called SleepPPG-Net2, which employs a multi-source domain training approach.SleepPPG-Net2 was benchmarked against two state-of-the-art models. Results: SleepPPG-Net2 showed consistently higher performance over benchmark approaches, with generalization performance (Cohen's kappa) improving by up to 19%. Performance disparities were observed in relation to age, sex, and sleep apnea severity. Conclusion: SleepPPG-Net2 sets a new standard for staging sleep from raw PPG time-series.

Related papers

• LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring [0.3141085922386211]
  This study introduces LG-Sleep, a novel subject-independent deep neural network architecture designed for mice sleep scoring through electroencephalogram (EEG) signals. LG-Sleep extracts local and global temporal transitions within EEG signals to categorize sleep data into three stages: wake, rapid eye movement (REM) sleep, and non-rapid eye movement (NREM) sleep.
  arXiv  Detail & Related papers  (2024-12-19T21:39:57Z)
• Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data [67.60224656603823]
  Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
  arXiv  Detail & Related papers  (2023-01-17T15:18:45Z)
• L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging [16.96499618061823]
  L-SeqSleepNet is a new deep learning model that takes into account whole-cycle sleep information for sleep staging. L-SeqSleepNet is able to alleviate the predominance of N2 sleep to bring down errors in other sleep stages.
  arXiv  Detail & Related papers  (2023-01-09T15:44:43Z)
• ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation [4.014524824655106]
  This work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint-temporal graphs. The proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.
  arXiv  Detail & Related papers  (2022-12-09T14:34:58Z)
• SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography [0.0]
  We developed Sleep-Net, a DL model for 4-class sleep staging from the raw PPG time series. We benchmarked the performance of Sleep-Net against models based on the best-reported state-of-the-art (SOTA) algorithms.
  arXiv  Detail & Related papers  (2022-02-11T16:17:42Z)
• Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal [63.18666008322476]
  Sleep problems are one of the major diseases all over the world. Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. Specialists have to score the different signals according to one of the standard guidelines.
  arXiv  Detail & Related papers  (2021-03-30T09:59:56Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• RobustSleepNet: Transfer learning for automated sleep staging at scale [0.0]
  Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. In practice, sleep stage classification relies on the visual inspection of 30-seconds epochs of polysomnography signals. We introduce RobustSleepNet, a deep learning model for automatic sleep stage classification able to handle arbitrary PSG montages.
  arXiv  Detail & Related papers  (2021-01-07T09:39:08Z)
• Automatic detection of microsleep episodes with deep learning [55.41644538483948]
  Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs) maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. MSEs are mostly not considered in the absence of established scoring criteria defining MSEs. We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
  arXiv  Detail & Related papers  (2020-09-07T11:38:40Z)
• Pairwise Relation Learning for Semi-supervised Gland Segmentation [90.45303394358493]
  We propose a pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images. This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net) We evaluate our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset.
  arXiv  Detail & Related papers  (2020-08-06T15:02:38Z)
• AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching [76.4844593082362]
  We investigate the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong baseline for remote HR measurement with architecture search (NAS) Comprehensive experiments are performed on three benchmark datasets on both intra-temporal and cross-dataset testing.
  arXiv  Detail & Related papers  (2020-04-26T05:43:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.