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Applied Artificial Intelligence
An International Journal
Volume 30, 2016 - Issue 6: Intelligent Methods Applied to Healthcare Information Systems
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Articles

Machine Learning Techniques Applied to Data Analysis and Anomaly Detection in ECG Signals

Tomasz AndrysiakInstitute of Telecommunications and Computer Science, UTP University of Science and Technology, Bydgoszcz, PolandCorrespondence[email protected]
Pages 610-634 | Published online: 21 Jul 2016

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Per Lynggaard. (2019) Detecting Premature Ventricular Contraction by Using Regulated Discriminant Analysis with Very Sparse Training Data. Applied Artificial Intelligence 33:3, pages 229-248.
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Anday Duru, Kadir Ileri, Fatih Karayurek & Ismail Rakip Karas. (2023) A New Method for Automatic Detection and Quantification of Dentinal Tubules. A New Method for Automatic Detection and Quantification of Dentinal Tubules.
Shinu M. Rajagopal, Supriya M.Rajkumar Buyya. (2023) FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments. Internet of Things 22, pages 100784.
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Siyu Wang, Min Li & Soo Boon Ng. (2022) Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information Statistics. Frontiers in Public Health 10.
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Omneya Attallah. (2022) An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques. Biosensors 12:5, pages 299.
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Robert Burduk & Jedrzej Biedrzycki. (2022) Subspace-based decision trees integration. Information Sciences 592, pages 215-226.
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Sanjeev Kumar Saini & Rashmi Gupta. (2021) Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges. Artificial Intelligence Review 55:2, pages 1519-1565.
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Jędrzej Biedrzycki & Robert Burduk. (2020) Decision Tree Integration Using Dynamic Regions of Competence. Entropy 22:10, pages 1129.
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Peng Zhan, Haoran Xu & Lin Chen. (2020) FCAD: Feature-based Clipped Representation for Time Series Anomaly Detection. FCAD: Feature-based Clipped Representation for Time Series Anomaly Detection.
Tomasz Andrysiak. (2018) Sparse representation and overcomplete dictionary learning for anomaly detection in electrocardiograms. Neural Computing and Applications 32:5, pages 1269-1285.
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Rostom Mennour & Mohamed Batouche. 2020. Deep Learning and Neural Networks. Deep Learning and Neural Networks 633 653 .
Rostom Mennour & Mohamed Batouche. (2018) Novel Scalable Deep Learning Approaches for Big Data Analytics Applied to ECG Processing. International Journal of Applied Metaheuristic Computing 9:4, pages 33-51.
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Huorong Ren, Xiujuan Liao, Zhiwu Li & Abdulrahman AI-Ahmari. (2017) Anomaly detection using piecewise aggregate approximation in the amplitude domain. Applied Intelligence 48:5, pages 1097-1110.
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Tomasz Andrysiak. 2018. International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 428 438 .

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