Hypergraph and cross-attention-based unsupervised domain adaptation framework for cross-domain myocardial infarction localization

Yuan, Shuaiying; He, Ziyang; Zhao, Jianhui; Yuan, Zhiyong; Alhudhaif, Adi; Alenezi, Fayadh

doi:10.1016/j.ins.2023.03.078

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…ECG recordings were annotated by two cardiologists, indicating the possibility of a specific label. Consistent with the rules for selecting the PTB-XL training database by Yuan et al (2023), we chose the same five classes of acute and subacute MI as the PTB database to study MI localization with single area, which is annotated with a probability of MI of 100%. The MI records selected in this study came from 605 MI patients.…”

Section: Databasesmentioning

confidence: 99%

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge

Li,

Huang,

Ning

et al. 2024

Physiol. Meas.

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Objective:Myocardial infarction (MI) is a serious cardiovascular disease that can cause irreversible damage to the heart, making early identification and treatment crucial. However, automatic MI detection and localization from an electrocardiogram (ECG) remain challenging. In this study, we propose two models, MFB-SENET and MFB-DMIL, for MI detection and localization, respectively. Approach: The MFB-SENET model is designed to detect MI, while the MFB-DMIL model is designed to localize MI. The MI localization model employs a specialized attention mechanism to integrate multi-instance learning with domain knowledge. This approach incorporates handcrafted features and introduces a new loss function called lead-loss, to improve MI localization. Grad-CAM is employed to visualize the decision-making process.Main Results:The proposed method was evaluated on the PTB and PTB-XL databases. Under the inter-patient scheme, the accuracy of MI detection and localization on the PTB database reached 93.88% and 67.17%, respectively. The accuracy of MI detection and localization on the PTB-XL database were 94.89% and 85.83%, respectively. Significance: Our method achieved comparable or better performance than other state-of-the-art algorithms. The proposed method combined deep learning and medical domain knowledge, demonstrates effectiveness and reliability, holding promise as an efficient MI diagnostic tool to assist physicians in formulating accurate diagnoses.

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Section: Databasesmentioning

confidence: 99%

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge

Li,

Huang,

Ning

et al. 2024

Physiol. Meas.

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“…They also utilized cluster-aligning and maintaining losses to regulate and structure feature information of the source and target data in invariant space. Yuan et al [4] introduced a hypergraph-based UDA method that exploits crossattention dual-channel networks in adaptation tasks. They also used a domain alignment method based on Wasserstein distance for edge features, which also applies a pseudolabel generation technique for retaining category-level fine-grained information of the distribution.…”

Section: Uda For Ecg Signal Classificationmentioning

confidence: 99%

“…On the other hand, the morphology and characteristics of different patients' ECG signals have diversity due to different physiological situations. Some of the same diseases can have different patterns in the ECG signals [4], which can be difficult to interpret or annotate for generating training samples' deep learning models. Recently, deep learning (DL) appears with strong baseline capabilities in many real-life applications.…”

Section: Introductionmentioning

confidence: 99%

SF-ECG: Source-Free Intersubject Domain Adaptation for Electrocardiography-Based Arrhythmia Classification

Rafi

2023

Applied Sciences

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Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly imbalanced and have regularization to use real-time patient data due to privacy concerns. Traditional models do not generalize on unseen cases and are also unable to preserve data privacy. Which incentivizes performance degradation in existing models with privacy limitations. To tackle generalization and privacy issues together, we introduce the framework SF-ECG, a source-free domain adaptation approach for patient-specific ECG classification. This framework does not require source data during adaptation, which solves the privacy issue during adaptation. We adopt a generative model (GAN) that learns to synthesize patient-specific ECG data in data-inefficient classes to make additional source data for imbalanced classes. Then, we use the local structure clustering method to strongly align target ECG features with similar neighbors. After seizing clustered target features, we use a classifier that is trained on source data with generated source samples, which makes the model generalizable in classifying unseen data. Empirical results under different experimental conditions in various interdomain datasets prove that the proposed framework achieves 0.8% improvements in UDA settings, along with preserving privacy and generalizability.

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Multi-source refined adversarial domain adaptation with transfer complementarity infusion for IoT intrusion detection under limited samples

Li,

Ma,

Duan

et al. 2024

Expert Systems with Applications

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Hypergraph and cross-attention-based unsupervised domain adaptation framework for cross-domain myocardial infarction localization

Cited by 8 publications

References 26 publications

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge

Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge

SF-ECG: Source-Free Intersubject Domain Adaptation for Electrocardiography-Based Arrhythmia Classification

Multi-source refined adversarial domain adaptation with transfer complementarity infusion for IoT intrusion detection under limited samples

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