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

Huang, Guanjie; Ma, Fenglong

doi:10.1007/978-3-030-86517-7_5

Cited by 14 publications

(4 citation statements)

References 32 publications

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“…Our method performed better than other existing methods in all the evaluated metrics except the Sn and F1 metrics, which were slightly worse (1%) compared to the method proposed by Almutairi et al (2021). Several studies using extended segments, such as SE-MSCNN (Chen et al 2022b), ConCAD (Huang and Ma 2021), and CNN-BiGRU (Chen et al 2022a), performed well, suggesting a positive effect of feeding the original and extended segments together into an effective deep-shallow fusion network.…”

Section: Apnea-ecg Datasetmentioning

confidence: 75%

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network

Li,

Ma,

Yue

et al. 2024

Physiol. Meas.

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An increasing number of people are being influenced by sleep apnea (SA), a disease that brings complications such as hypertension and arrhythmia. Single-lead electrocardiogram (ECG) SA detection based on portable devices is of increased interest as an alternative to the expensive and time-consuming polysomnography (PSG) performed in hospitals, because of its low cost and the use of light-weight portable devices. In single-lead ECG SA detection, researchers have found that considering the neighborhood information of the ECG segment can improve detection performance. However, existing work fails to reduce the impact of noise in the neighborhood information. To address this issue, we propose a effective deep-shallow fusion network, EDSFnet, with simple architecture. We use a deeper residual network to extract higher-level features of the original ECG segments, which are semantically strong and contain less noise, and lower level features with high resolution, containing more detailed neighborhood information fro- m ECG segments. Effective channel attention (ECA) is then used to fuse these two types of features to exploit their complementary nature. EDSFnet is state-of-the-art on the PhysioNet Apnea-ECG dataset, with accuracy of 92.6% per-segment, and 100% per-recording. EDSFnet achieved competitive results in subject-independent experiments on the FAH-ECG clinical dataset.

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Section: Apnea-ecg Datasetmentioning

confidence: 75%

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network

Li,

Ma,

Yue

et al. 2024

Physiol. Meas.

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“…The authors of [177] proposed to use both expert features and DNN features. They proposed a contrastive-based cross-attention model to predict sleep apnea through ECG signals.…”

Section: Contrastive Learningmentioning

confidence: 99%

“…Then, the fused feature vector goes through the projection for contrastive loss and it is passed to the classifiers for apnea detection. [177]. Image taken from [177].…”

Section: Contrastive Learningmentioning

confidence: 99%

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

Soleimani,

Barahona,

Chen

et al. 2023

Physiologia

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Sleep staging has a very important role in diagnosing patients with sleep disorders. In general, this task is very time-consuming for physicians to perform. Deep learning shows great potential to automate this process and remove physician bias from decision making. In this study, we aim to identify recent trends on performance improvement and the causes for these trends. Recent papers on sleep stage classification and interpretability are investigated to explore different modeling and data manipulation techniques, their efficiency, and recent advances. We identify an improvement in performance up to 12% on standard datasets over the last 5 years. The improvements in performance do not appear to be necessarily correlated to the size of the models, but instead seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning.

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“…While DL has been effectively employed in many neuroscience applications, challenges remain with the quality and availability of the data (Banville et al, 2021;Younes, 2017;Huang and Ma, 2021). Recently, contrastive learning has shown to be an effective Self-Supervised Learning (SSL) technique to address the issues of the limitation of the data availability, noisy labels, and noisy data (He et al, 2020;Zbontar et al, 2021;Grill et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Towards Metacognition: Subject-Aware Contrastive Deep Fusion Representation Learning for EEG Analysis

Briden

Norouzi

2022

Preprint

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We propose a subject-aware contrastive learning deep fusion neural network framework for effectively classifying subjects' confidence levels in the perception of visual stimuli. The framework, called WaveFusion, is composed of lightweight convolutional neural networks for per-lead time-frequency analysis and an attention network for integrating the lightweight modalities for final prediction. To facilitate the training of WaveFusion, we incorporate a subject-aware contrastive learning approach by taking advantage of the heterogeneity within a multi-subject electroencephalogram dataset to boost representation learning and classification accuracy. The WaveFusion framework demonstrates high accuracy in classifying confidence levels by achieving a classification accuracy of 95.7% while also identifying influential brain regions.

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

Cited by 14 publications

References 32 publications

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

Towards Metacognition: Subject-Aware Contrastive Deep Fusion Representation Learning for EEG Analysis

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