A generative foundation model for five-class sleep staging with arbitrary sensor input

• URL: http://arxiv.org/abs/2408.15253v1
• Date: Fri, 9 Aug 2024 08:09:28 GMT
• Title: A generative foundation model for five-class sleep staging with arbitrary sensor input
• Authors: Hans van Gorp, Merel M. van Gilst, Pedro Fonseca, Fokke B. van Meulen, Johannes P. van Dijk, Sebastiaan Overeem, Ruud J. G. van Sloun,
• Abstract summary: Gold-standard sleep scoring is based on a subset of PSG signals, namely the EEG, EOG, and EMG. The PSG consists of many more signal derivations that could potentially be used to perform sleep staging, including cardiac and respiratory modalities. This paper proposes a deep generative foundation model for fully automatic sleep staging from a plurality of sensors and any combination thereof.
• Score: 14.146442985487598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gold-standard sleep scoring as performed by human technicians is based on a subset of PSG signals, namely the EEG, EOG, and EMG. The PSG, however, consists of many more signal derivations that could potentially be used to perform sleep staging, including cardiac and respiratory modalities. Leveraging this variety in signals would offer advantages, for example by increasing reliability, resilience to signal loss, and application to long-term non-obtrusive recordings. This paper proposes a deep generative foundation model for fully automatic sleep staging from a plurality of sensors and any combination thereof. We trained a score-based diffusion model with a transformer backbone using a dataset of 1947 expert-labeled overnight sleep recordings with 36 different signals, including neurological, cardiac, and respiratory signals. We achieve zero-shot inference on any sensor set by using a novel Bayesian factorization of the score function across the sensors, i.e., it does not require retraining on specific combinations of signals. On single-channel EEG, our method reaches the performance limit in terms of PSG inter-rater agreement (5-class accuracy 85.6%, kappa 0.791). At the same time, the method offers full flexibility to use any sensor set derived from other modalities, for example, as typically used in home recordings that include finger PPG, nasal cannula and thoracic belt (5-class accuracy 79.0%, kappa of 0.697), or by combining derivations not typically used for sleep staging such as the tibialis and sternocleidomastoid EMG (5-class accuracy 71.0%, kappa of 0.575). Additionally, we propose a novel interpretability metric in terms of information gain per sensor and show that this is linearly correlated with classification performance. Lastly, our foundation model allows for post-hoc addition of entirely new sensor modalities by merely training a score estimator on the novel input.

Related papers

• MobileNetV2: A lightweight classification model for home-based sleep apnea screening [3.463585190363689]
  This study proposes a novel lightweight neural network model leveraging features extracted from electrocardiogram (ECG) and respiratory signals for early OSA screening. ECG signals are used to generate feature spectrograms to predict sleep stages, while respiratory signals are employed to detect sleep-related breathing abnormalities. By integrating these predictions, the method calculates the apnea-hypopnea index (AHI) with enhanced accuracy, facilitating precise OSA diagnosis.
  arXiv  Detail & Related papers  (2024-12-28T01:37:25Z)
• Single Channel EEG Based Insomnia Identification Without Sleep Stage Annotations [0.3495246564946556]
  The performance of the model is validated using 50 insomnia patients and 50 healthy subjects. The developed model has the potential to simplify current sleep monitoring systems and enable in-home ambulatory monitoring.
  arXiv  Detail & Related papers  (2024-02-09T08:59:37Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring [63.30661835412352]
  This paper explores and compares the convenience of using additional signals apart from electroencephalograms. The best overall model, an ensemble of Depth-wise Separational Convolutional Neural Networks, has achieved an accuracy of 86.06%.
  arXiv  Detail & Related papers  (2021-07-23T06:37:38Z)
• Sleep Staging Based on Serialized Dual Attention Network [0.0]
  We propose a deep learning model SDAN based on raw EEG. It serially combines the channel attention and spatial attention mechanisms to filter and highlight key information. It achieves excellent results in the N1 sleep stage compared to other methods.
  arXiv  Detail & Related papers  (2021-07-18T13:18:12Z)
• 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)
• Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals [0.0]
  We develop a network architecture with the aim of classifying the sleep stage in awake or asleep using only HR signals from a pulse oximeter. Transformers are able to model the sequence, learning the transition rules between sleep stages. The overall accuracy, specificity, sensibility, and Cohen's Kappa coefficient were 90.0%, 94.9%, 78.1%, and 0.73.
  arXiv  Detail & Related papers  (2021-01-29T22:58:33Z)
• 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)
• Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications [71.1911136637719]
  We show how provable guarantees can be naturally applied to other real world settings. We show how specific intervals of fuel injection quantities can be targeted to maximize robustness for certain ranges.
  arXiv  Detail & Related papers  (2020-06-30T23:33:17Z)
• Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network [0.19686770963118383]
  The study presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The model is constructed using convolutional, max pooling layers and a fully connected Multilayer Perceptron (MLP) consisting of a hidden layer and SoftMax output for classification. This demonstrates the model can identify the presence of Apnoea with a high degree of accuracy.
  arXiv  Detail & Related papers  (2020-02-03T15:47:00Z)

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.