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Detecting ECG abnormalities via transductive transfer learning

Published: 07 October 2012 Publication History

Abstract

Detecting Electrocardiogram (ECG) abnormalities is the process of identifying irregular cardiac activities which may lead to severe heart damage or even sudden death. Due to the rapid development of cyberphysic systems and health informatics, embedding the function of ECG abnormality detection to various devices for real time monitoring has attracted more and more interest in the past few years. The existing machine learning and pattern recognition techniques developed for this purpose usually require sufficient labeled training data for each user. However, obtaining such supervised information is difficult, which makes the proposed ECG monitoring function unrealistic.
To tackle the problem, we take advantage of existing well labeled ECG signals and propose a transductive transfer learning framework for the detection of abnormalities in ECG. In our model, unsupervised signals from target users are classified with knowledge transferred from the supervised source signals. In the experimental evaluation, we implemented our method on the MIT-BIH Arrhythmias Dataset and compared it with both anomaly detection and transductive learning baseline approaches. Extensive experiments show that our proposed algorithm remarkably outperforms all the compared methods, proving the effectiveness of it in detecting ECG abnormalities.

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cover image ACM Conferences
BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
October 2012
725 pages
ISBN:9781450316705
DOI:10.1145/2382936
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 October 2012

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Author Tags

  1. ECG
  2. anomaly detection
  3. transductive learning

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BCB '12 Paper Acceptance Rate 33 of 159 submissions, 21%;
Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

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  • (2024)Research on carotid artery plaque anomaly detection algorithm based on ultrasound imagesComputers in Biology and Medicine10.1016/j.compbiomed.2024.109180182(109180)Online publication date: Nov-2024
  • (2023)Anomaly Detection of ECG Time Series Signal Using Auto Encoders Neural Network2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA)10.1109/ICCUBEA58933.2023.10392094(1-7)Online publication date: 18-Aug-2023
  • (2022)Detection of Abnormal Cardiac Response Patterns in Cardiac Tissue Using Deep LearningMathematics10.3390/math1015278610:15(2786)Online publication date: 5-Aug-2022
  • (2022)Abnormal ECG detection based on an adversarial autoencoderFrontiers in Physiology10.3389/fphys.2022.96172413Online publication date: 2-Sep-2022
  • (2022)Real-time data analysis in health monitoring systemsJournal of Biomedical Informatics10.1016/j.jbi.2022.104009127:COnline publication date: 1-Mar-2022
  • (2021)On Inductive-Transductive Learning with Graph Neural NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2021.3054304(1-1)Online publication date: 2021
  • (2020)A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG)Sensors10.3390/s2005146120:5(1461)Online publication date: 6-Mar-2020
  • (2020)A Transfer Learning Framework for Anomaly Detection Using Model of Normality2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)10.1109/IEMCON51383.2020.9284916(0055-0061)Online publication date: 4-Nov-2020
  • (2020)Decision Boundary-based Anomaly Detection Model using Improved AnoGAN from ECG DataIEEE Access10.1109/ACCESS.2020.3000638(1-1)Online publication date: 2020
  • (2019)Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2019.29219127(81664-81681)Online publication date: 2019
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