CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to
Imperfect Modalities
- URL: http://arxiv.org/abs/2304.06485v1
- Date: Mon, 27 Mar 2023 18:28:58 GMT
- Title: CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to
Imperfect Modalities
- Authors: Konstantinos Kontras, Christos Chatzichristos, Huy Phan, Johan
Suykens, Maarten De Vos
- Abstract summary: CoRe-Sleep is a Coordinated Representation multimodal fusion network.
We show how appropriately handling multimodal information can be the key to achieving such robustness.
This work aims at bridging the gap between automated analysis tools and their clinical utility.
- Score: 10.347153539399836
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Sleep abnormalities can have severe health consequences. Automated sleep
staging, i.e. labelling the sequence of sleep stages from the patient's
physiological recordings, could simplify the diagnostic process. Previous work
on automated sleep staging has achieved great results, mainly relying on the
EEG signal. However, often multiple sources of information are available beyond
EEG. This can be particularly beneficial when the EEG recordings are noisy or
even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated
Representation multimodal fusion network that is particularly focused on
improving the robustness of signal analysis on imperfect data. We demonstrate
how appropriately handling multimodal information can be the key to achieving
such robustness. CoRe-Sleep tolerates noisy or missing modalities segments,
allowing training on incomplete data. Additionally, it shows state-of-the-art
performance when testing on both multimodal and unimodal data using a single
model on SHHS-1, the largest publicly available study that includes sleep stage
labels. The results indicate that training the model on multimodal data does
positively influence performance when tested on unimodal data. This work aims
at bridging the gap between automated analysis tools and their clinical
utility.
Related papers
- MSSC-BiMamba: Multimodal Sleep Stage Classification and Early Diagnosis of Sleep Disorders with Bidirectional Mamba [5.606144017978037]
We develop an automated model for sleep staging and disorder classification to enhance diagnostic accuracy and efficiency.
Considering the characteristics of polysomnography (PSG) multi-lead sleep monitoring, we designed a multimodal sleep state classification model, MSSC-BiMamba.
The model is the first to apply BiMamba to sleep staging with multimodal PSG data, showing substantial gains in computational and memory efficiency.
arXiv Detail & Related papers (2024-05-30T15:16:53Z) - Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
We propose a new method called Convolutional Monge Mapping Normalization (CMMN)
CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data.
Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
arXiv Detail & Related papers (2023-05-30T08:24:01Z) - DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and
Temporal Relatedness [78.98998551326812]
We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors.
We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis.
We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
arXiv Detail & Related papers (2022-09-26T21:59:14Z) - SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive
Learning [0.0]
We propose a deep learning framework named SleePyCo that incorporates 1) a feature pyramid and 2) supervised contrastive learning for automatic sleep scoring.
For the feature pyramid, we propose a backbone network named SleePyCo-backbone to consider multiple feature sequences on different temporal and frequency scales.
Supervised contrastive learning allows the network to extract class discriminative features by minimizing the distance between intra-class features and simultaneously maximizing that between inter-class features.
arXiv Detail & Related papers (2022-09-20T04:10:49Z) - 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) - 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)
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.