Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning

Xinyu Huang*, Franziska Schmelter, Muhammad Tausif Irshad, Artur Piet, Muhammad Adeel Nisar, Christian Sina, Marcin Grzegorzek

*Korrespondierende/r Autor/-in für diese Arbeit

Abstract

Sleep is an important research area in nutritional medicine that plays a crucial role in human physical and mental health restoration. It can influence diet, metabolism, and hormone regulation, which can affect overall health and well-being. As an essential tool in the sleep study, the sleep stage classification provides a parsing of sleep architecture and a comprehensive understanding of sleep patterns to identify sleep disorders and facilitate the formulation of targeted sleep interventions. However, the class imbalance issue is typically salient in sleep datasets, which severely affects classification performances. To address this issue and to extract optimal multimodal features of EEG, EOG, and EMG that can improve the accuracy of sleep stage classification, a Borderline Synthetic Minority Oversampling Technique (B-SMOTE)-Based Supervised Convolutional Contrastive Learning (BST-SCCL) is proposed, which can avoid the risk of data mismatch between various sleep knowledge domains (varying health conditions and annotation rules) and strengthening learning characteristics of the N1 stage from the pair-wise segments comparison strategy. The lightweight residual network architecture with a novel truncated cross-entropy loss function is designed to accommodate multimodal time series and boost the training speed and performance stability. The proposed model has been validated on four well-known public sleep datasets (Sleep-EDF-20, Sleep-EDF-78, ISRUC-1, and ISRUC-3) and its superior performance (overall accuracy of 91.31–92.34%, MF1 of 88.21–90.08%, and Cohen's Kappa coefficient k of 0.87–0.89) has further demonstrated its effectiveness. It shows the great potential of contrastive learning for cross-domain knowledge interaction in precision medicine.

OriginalspracheEnglisch
Aufsatznummer107501
ZeitschriftComputers in Biology and Medicine
Jahrgang166
ISSN0010-4825
DOIs
PublikationsstatusVeröffentlicht - 11.2023

Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Forschungsschwerpunkt: Gehirn, Hormone, Verhalten - Center for Brain, Behavior and Metabolism (CBBM)

DFG-Fachsystematik

  • 205-17 Endokrinologie, Diabetologie, Metabolismus
  • 206-08 Kognitive und Systemische Humanneurowissenschaften
  • 110-05 Differentielle, Klinische Psychologie, und Medizinische Psychologie, Methoden
  • 409-05 Interaktive und intelligente Systeme, Bild- und Sprachverarbeitung, Computergraphik und Visualisierung

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