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An Ensemble Model for Mobile Device based Arrhythmia Detection

Published: 22 September 2013 Publication History

Abstract

Recent advances in smart mobile device technology have resulted in global availability of portable computing devices capable of performing many complex functions. With the ultimate intent of promoting human's well-being, mobile device based arrhythmia detection (MAD) has attracted lots of attention recently. Without any guidance or supervision from experts, the performance of arrhythmia detection is usually unsatisfactory. Supervised learning can learn from labeled cardiac cycles to detect arrhythmias for each mobile device user if enough training data is provided. However, it is time-consuming, costly and sometimes impossible to let experts annotate enough training data for each user. To tackle this problem, we take advantage of publicly available and well annotated data to infer knowledge which can be treated as experts for MAD. To reduce the space usage of the framework, we extract from each source of labeled data an expert model, which consists of a task-independent individual characteristic vector and a task-related preference vector. Multiple experts are then integrated into an ensemble model for arrhythmia detection. Both space and time complexities of this proposed approach are theoretically analyzed and experimentally examined. To evaluate the performance of the method, we implement it on the MIT-BIH Arrhythmia Dataset and compare it with seven state-of-the-art methods in the area. Extensive experimental results show that the proposed algorithm outperforms all the baseline methods, which validates the effectiveness of the proposed algorithm in MAD.

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cover image ACM Conferences
BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
September 2013
987 pages
ISBN:9781450324342
DOI:10.1145/2506583
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: 22 September 2013

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Author Tags

  1. Arrhythmia Detection
  2. ECG
  3. Ensemble Model

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BCB'13
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BCB'13: ACM-BCB2013
September 22 - 25, 2013
Wshington DC, USA

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BCB'13 Paper Acceptance Rate 43 of 148 submissions, 29%;
Overall Acceptance Rate 254 of 885 submissions, 29%

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