A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification

He, Ziyang; Chen, Yufei; Yuan, Shuaiying; Zhao, Jianhui; Yuan, Zhiyong; Polat, Kemal; Alhudhaif, Adi; Alenezi, Fayadh; Hamid, Arwa

doi:10.1016/j.eswa.2023.119711

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…He et al [26] introduced a new method, multi-level unsupervised domain adaptation framework (MLUDAF), for diagnosing ARRs. They used the spatial pyramid pooling residual (ASPP-R) module to extract spatio-temporal features and employed the graph convolutional network (GCN) module for data structure features.…”

Section: A Methodology Overview In Previous Studiesmentioning

confidence: 99%

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

Bourkha,

Hatim,

Nasir

et al. 2024

IJACSA

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The World Health Organization (WHO) sheds light on the escalating prevalence of heart diseases, foreseeing a substantial rise in the years ahead, impacting a vast global population. Swift and accurate early detection becomes pivotal in managing severe complications, underscoring the urgency of timely identification. While Ventricular Ectopic Beats (V) might initially be considered normal, their frequent occurrence could serve as a potential red flag for progressing to severe conditions like atrial fibrillation, Ventricular Tachycardia, and even cardiac arrest. This accentuates the need for developing an automated approach for early detection of cardiovascular diseases (CVD). This paper presents a novel method to classify arrhythmias. Leveraging the Wavelet Scattering Transform (WST) to extract morphological features from Electrocardiogram heartbeats (ECG), these features seamlessly integrate into a 1D Convolutional Neural Network (CNN).The CNN is finely tuned to distinguish between V, Supraventricular Ectopic Beats (S), and Non-Ectopic Beats (N). Our model's performance surpasses state-of-the-art models, boasting precision, sensitivity, and specificity of 94.56%, 97.26%, and 99.54% for V, and 99.25%, 98.65%, and 93.26% for N. Remarkably, it achieves 68.01% precision, 77.75% sensitivity, and 99.14% specificity for S.

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Section: A Methodology Overview In Previous Studiesmentioning

confidence: 99%

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

Bourkha,

Hatim,

Nasir

et al. 2024

IJACSA

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“…In light of this data scarcity, alternative approaches offer promising avenues to overcome these limitations. These include employing shallow DL architectures, fine-tuning pretrained models [56] rather than building a model from scratch or adopting semi-supervised [8] or unsupervised learning techniques [9,36] for ECG data analysis.…”

Section: Remarksmentioning

confidence: 99%

“…ECG CAD systems can serve as a valuable tool for medical professionals, facilitating objective diagnosis [3]. The association between different ECG records can be established through supervised [7], semisupervised [8], or unsupervised [9] ML approaches. Supervised learning entails training a model on a labeled dataset where ground-truth labels are known for each record.…”

Section: Introductionmentioning

confidence: 99%

Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database

Alinsaif

2024

Computation

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Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisions made by cardiologists. This review paper surveys diverse computational intelligence methodologies employed for arrhythmia analysis within the context of the widely utilized MIT-BIH dataset. The paucity of adequately annotated medical datasets significantly impedes advancements in various healthcare domains. Publicly accessible resources such as the MIT-BIH Arrhythmia Database serve as invaluable tools for evaluating and refining computer-assisted diagnosis (CAD) techniques specifically targeted toward arrhythmia detection. However, even this established dataset grapples with the challenge of class imbalance, further complicating its effective analysis. This review explores the current research landscape surrounding the application of graph-based approaches for both anomaly detection and classification within the MIT-BIH database. By analyzing diverse methodologies and their respective accuracies, this investigation aims to empower researchers and practitioners in the field of ECG signal analysis. The ultimate objective is to refine and optimize CAD algorithms, ultimately culminating in improved patient care outcomes.

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“…In recent years, deep learning and intelligent algorithms has developed rapidly [50][51][52][60][61][62], most current approaches utilize deep neural networks to extract both global and local representation from individuals [14][15][16]. Zheng et al [15] treat each person as a separate class and designed a multi-class loss function to allow the network to extract discriminative global features.…”

Section: Related Work 21 General Person Re-idmentioning

confidence: 99%

Attention-Enhanced Controllable Disentanglement for Cloth-Changing Person Re-identification

Ge,

Yu,

Chen

et al. 2024

Preprint

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Cloth-changing person re-identification (Re-ID) is an emerging research theme that aims at identifying individuals after clothing change. Many contemporary approaches focus on disentangling clothing features and solely employ clothing-unrelated parts for identification. However, the absence of ground truth poses a significant challenge to the disentanglement process, these methods may introduce unintended noise and degrade the overall performance. To mitigate this issue, we propose a novel framework, termed Attention-based Controllable Disentanglement Network (ACD-Net). In ACD-Net, we design an Attention-enhanced Disentanglement Branch (ADB) where human parsing masks are introduced to guide the separation of clothing features and clothing-unrelated features. Here, clothing-unrelated features are subdivided into unclothed body features and contour features, and we propose two novel attention mechanisms: Dynamic Interaction-Remote Aggregation Attention (DI-RAA) and Dynamic Interaction-Positional Relevance Attention (DI-PRA) to enhance the representations of these two features, respectively. Experimental results on PRCC, LTCC, DeepChange, and CCVID datasets demonstrate the superiority of our approach over the state-of-the-art methods. For the cloth-changing setting, the mAP of our network on PRCC, LTCC, and DeepChangedatasets are 59.5%, 22.6%, and 20.6%, and the Rank-1 are 60.6%, 45.5%, and 56.8%, respectively. In addition, our model also obtains 81.5% of mAP and 83.4% of Rank-1 on the video dataset CCVID. The code is available at: https://github.com/jk-love-ge/ACDNet.

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A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification

Cited by 19 publications

References 43 publications

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

A Novel Inter Patient ECG Arrhythmia Classification Approach with Deep Feature Extraction and 1D Convolutional Neural Network

Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database

Attention-Enhanced Controllable Disentanglement for Cloth-Changing Person Re-identification

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