Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal

• URL: http://arxiv.org/abs/2103.16215v1
• Date: Tue, 30 Mar 2021 09:59:56 GMT
• Title: Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal
• Authors: Enrique Fernandez-Blanco, Daniel Rivero, Alejandro Pazos
• Abstract summary: 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.
• Score: 63.18666008322476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. After its recording, the specialists have to score the different signals according to one of the standard guidelines. This process is carried out manually, which can be highly time-consuming and very prone to annotation errors. Therefore, over the years, many approaches have been explored in an attempt to support the specialists in this task. In this paper, an approach based on convolutional neural networks is presented, where an in-depth comparison is performed in order to determine the convenience of using more than one signal simultaneously as input. Additionally, the models were also used as parts of an ensemble model to check whether any useful information can be extracted from signal processing a single signal at a time which the dual-signal model cannot identify. Tests have been performed by using a well-known dataset called expanded sleep-EDF, which is the most commonly used dataset as the benchmark for this problem. The tests were carried out with a leave-one-out cross-validation over the patients, which ensures that there is no possible contamination between training and testing. The resulting proposal is a network smaller than previously published ones, but which overcomes the results of any previous models on the same dataset. The best result shows an accuracy of 92.67\% and a Cohen's Kappa value over 0.84 compared to human experts.

Related papers

• wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals [0.6261444979025643]
  wav2sleep is a unified model designed to operate on variable sets of input signals during training and inference. It outperforms existing sleep stage classification models across test-time input combinations including ECG, PPG, and respiratory signals.
  arXiv  Detail & Related papers  (2024-11-07T12:01:36Z)
• Recurrent and Convolutional Neural Networks in Classification of EEG Signal for Guided Imagery and Mental Workload Detection [0.9895793818721335]
  This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier.
  arXiv  Detail & Related papers  (2024-05-27T07:49:30Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities [10.347153539399836]
  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.
  arXiv  Detail & Related papers  (2023-03-27T18:28:58Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• 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)
• 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)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

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.