A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging

• URL: http://arxiv.org/abs/2112.07252v1
• Date: Tue, 14 Dec 2021 09:35:40 GMT
• Title: A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
• Authors: Vaibhav Joshi, Sricharan Vijayarangan, Preejith SP, and Mohanasankar Sivaprakasam
• Abstract summary: We propose a cross-modal KD framework to improve ECG-based sleep staging performance with assistance from features learned through models trained on EEG. The proposed model showed a 14.3% and 13.4% increase in weighted-F1-score in 4-class and 3-class sleep staging, respectively.
• Score: 1.181206257787103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automatic Sleep Staging study is presently done with the help of Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based approaches have enabled significant progress in this area, allowing for near-human accuracy in automated sleep staging. However, EEG based sleep staging requires an extensive as well as an expensive clinical setup. Moreover, the requirement of an expert for setup and the added inconvenience to the subject under study renders it unfavourable in a point of care context. Electrocardiogram (ECG), an unobtrusive alternative to EEG, is more suitable, but its performance, unsurprisingly, remains sub-par compared to EEG-based sleep staging. Naturally, it would be helpful to transfer knowledge from EEG to ECG, ultimately enhancing the model's performance on ECG based inputs. Knowledge Distillation (KD) is a renowned concept in DL that looks to transfer knowledge from a better but potentially more cumbersome teacher model to a compact student model. Building on this concept, we propose a cross-modal KD framework to improve ECG-based sleep staging performance with assistance from features learned through models trained on EEG. Additionally, we also conducted multiple experiments on the individual components of the proposed model to get better insight into the distillation approach. Data of 200 subjects from the Montreal Archive of Sleep Studies (MASS) was utilized for our study. The proposed model showed a 14.3\% and 13.4\% increase in weighted-F1-score in 4-class and 3-class sleep staging, respectively. This demonstrates the viability of KD for performance improvement of single-channel ECG based sleep staging in 4-class(W-L-D-R) and 3-class(W-N-R) classification.

Related papers

• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• ECGMamba: Towards Efficient ECG Classification with BiSSM [3.0120310355085467]
  We propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification.
  arXiv  Detail & Related papers  (2024-06-14T14:55:53Z)
• MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [41.324530807795256]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation. We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)
• Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification [1.565361244756411]
  We introduce an innovative approach to automated sleep stage classification using EOG signals, addressing the discomfort and impracticality associated with EEG data acquisition. Our proposed SE-Resnet-Transformer model provides an accurate classification of five distinct sleep stages from raw EOG signal.
  arXiv  Detail & Related papers  (2023-09-25T16:23:39Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation [0.8602553195689513]
  Electrocardiogram (ECG) is a more suitable alternative to EEG in sleep staging. Knowledge Distillation (KD) is a promising notion in Deep Learning (DL) that shares knowledge from a superior performing but usually more complex teacher model to an inferior but compact student model. This study demonstrates the feasibility of KD for single-channel ECG based sleep staging's performance enhancement in 3-class (W-R-N) and 4-class (W-R-L-D) classification.
  arXiv  Detail & Related papers  (2022-11-18T21:09:25Z)
• SEVGGNet-LSTM: a fused deep learning model for ECG classification [38.747030782394646]
  The input ECG signals are firstly segmented and normalized, and then fed into the combined VGG and LSTM network for feature extraction and classification. An attention mechanism (SE block) is embedded into the core network for increasing the weight of important features.
  arXiv  Detail & Related papers  (2022-10-31T07:36:48Z)
• A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data [3.382472583898067]
  Sleep studies using Polysomnography are associated with discomfort and often lower sleep quality. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies. We propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach.
  arXiv  Detail & Related papers  (2022-10-27T01:32:58Z)
• Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration [0.0]
  This article introduces two novel techniques to improve the performance of the current deep learning system for 3-lead ECG classification. Specifically, we propose a multi-task learning scheme in the form of the number of heartbeats regression and an effective mechanism to integrate patient demographic data into the system. With these two advancements, we got classification performance in terms of F1 scores of 0.9796 and 0.8140 on two large-scale ECG datasets.
  arXiv  Detail & Related papers  (2022-08-15T09:33:36Z)
• EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification [123.93460670568554]
  This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network. The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
  arXiv  Detail & Related papers  (2021-01-24T19:03:10Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

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.