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This thesis attempts at developing machine learning-based algorithms that aid physicians in the task of automatic ECG and iECG interpretation.
Feb 23, 2022 · However, the interpretation of ECG and iECG signals is a complex task that requires years of experience, difficulting the correct diagnosis for ...
Machine learning in the electrocardiogram - ScienceDirect
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This review describes the most recent machine learning-based systems applied to the electrocardiogram as well as pros and cons in the use of these techniques.
Feb 28, 2022 · This study reviews recent ECG clustering techniques with the focus on machine learning and deep learning algorithms. We critically review ...
Missing: quantification interpretation
Aug 7, 2021 · Deep learning is a promising technique to analyse resting ECG signals for the detection of structural cardiac pathologies.
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The aim of this work is to analyse and summarise as many as possible of the published studies that used ML algorithms and ECG data for the exemplary application ...
Missing: quantification | Show results with:quantification
Sep 12, 2024 · These studies collectively highlight the effectiveness of GAN-based models in generating high-quality, patient-specific synthetic ECG data, ...
Missing: quantification | Show results with:quantification
... In this proposal, we introduce the FECG SQA approach based on unsupervised learning to increase the accuracy of FHR estimation. Based on earlier research [9 ...
Feb 10, 2021 · This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, ...
Feb 28, 2022 · This study reviews recent ECG clustering techniques with the focus on machine learning and deep learning algorithms. We critically review and ...
Missing: quantification interpretation