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Cardiac arrhythmia 1

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Domain knowledge enhanced deep learning for electrocardiogram arrhythmia classification Research Article

Jie SUN,sunjie@nbut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1, Pages 59-72 doi: 10.1631/FITEE.2100519

Abstract: Deep learning provides an effective way for automatic classification of s, but in , pure data-driven methods working as black-boxes may lead to unsatisfactory results. A promising solution is combining with deep learning. This paper develops a flexible and extensible framework for integrating with a deep neural network. The model consists of a deep neural network to capture the statistical pattern between input data and the ground-truth label, and a knowledge module to guarantee consistency with the . These two components are trained interactively to bring the best of both worlds. The experiments show that the is valuable in refining the neural network prediction and thus improves accuracy.

Keywords： Domain knowledge Cardiac arrhythmia Electrocardiogram (ECG) Clinical decision-making

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A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG Regular Papers

Lu-di WANG, Wei ZHOU, Ying XING, Na LIU, Mahmood MOVAHEDIPOUR, Xiao-guang ZHOU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3, Pages 405-413 doi: 10.1631/FITEE.1700413

Abstract:

Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched inParticularly, the presented method shows outstanding performance in reconstructing the pathological ECGaccurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECGfrom a reduced lead-set ECG recording.

Keywords： Convolutional neural networks (CNNs) Electrocardiogram (ECG) synthesis E-health

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Frequency-domain Analysis of ECG Signals

Tu Chengyuan,Zeng Yanjun,Li Shuxin

Strategic Study of CAE 2002, Volume 4, Issue 12, Pages 66-70

Abstract:

A new simple approach to effectively detect QRS — T complexes in ECG curve is described, sopower spectrum function, the auto-correlation function and cross-correlation function, two kinds of ECG

Keywords： ECG curve P-wave f-wave AF (atrial fibrillation) histogram power spectrum autocorrelation cross-correlation