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ConCAD: Contrastive Learning-Based Cross Attention for Sleep Apnea Detection

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12979))

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Abstract

With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach. However, the hand-crafted expert knowledge-based features are still insightful. These expert-curated features can increase the model’s generalization and remind the model of some data characteristics, such as the time interval between two patterns. It is particularly advantageous in tasks with the clinically-relevant data, where the data are usually limited and complex. To keep both implicit deep features and expert-curated explicit features together, an effective fusion strategy is becoming indispensable. In this work, we focus on a specific clinical application, i.e., sleep apnea detection. In this context, we propose a contrastive learning-based cross attention framework for sleep apnea detection (named ConCAD). The cross attention mechanism can fuse the deep and expert features by automatically assigning attention weights based on their importance. Contrastive learning can learn better representations by keeping the instances of each class closer and pushing away instances from different classes in the embedding space concurrently. Furthermore, a new hybrid loss is designed to simultaneously conduct contrastive learning and classification by integrating a supervised contrastive loss with a cross-entropy loss. Our proposed framework can be easily integrated into standard deep learning models to utilize expert knowledge and contrastive learning to boost performance. As demonstrated on two public ECG dataset with sleep apnea annotation, ConCAD significantly improves the detection performance and outperforms state-of-art benchmark methods.

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Authors and Affiliations

  1. College of Information Sciences and Technology, Pennsylvania State University, State College, PA, 16802, USA

    Guanjie Huang & Fenglong Ma

Authors
  1. Guanjie Huang
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  2. Fenglong Ma
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Corresponding authors

Correspondence to Guanjie Huang or Fenglong Ma .

Editor information

Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

Appendix A

Appendix A

The feature extractor are different for different data and tasks. In this study, we design a CNN-based extractors for ECG, RRI and RPE separately. The structure of the extractor for two dataset are also different as their ECG data have different sampling frequency and noise. The details are shown in the table below. The ConvBlock(number of filters, kernel size, stride) is made of one convolutional layer, one batch normalization layers, one ReLU activation layer (Table 3).

Table 3. The details of the feature extractors used for ECG, RRI and RPE on Apnea-ECG and MIT-BIH PSG.
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Huang, G., Ma, F. (2021). ConCAD: Contrastive Learning-Based Cross Attention for Sleep Apnea Detection. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12979. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-86517-7_5

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