Article

Active learning applied to patient-adaptive heartbeat classification

Authors:

John V. GuttagAuthors Info & Claims

NIPS'10: Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 2

Pages 2442 - 2450

Published: 06 December 2010 Publication History

Abstract

While clinicians can accurately identify different types of heartbeats in electrocardiograms (ECGs) from different patients, researchers have had limited success in applying supervised machine learning to the same task. The problem is made challenging by the variety of tasks, inter- and intra-patient differences, an often severe class imbalance, and the high cost of getting cardiologists to label data for individual patients. We address these difficulties using active learning to perform patient-adaptive and task-adaptive heartbeat classification. When tested on a benchmark database of cardiologist annotated ECG recordings, our method had considerably better performance than other recently proposed methods on the two primary classification tasks recommended by the Association for the Advancement of Medical Instrumentation. Additionally, our method required over 90% less patient-specific training data than the methods to which we compared it.

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Cited By

Thiam PMeudt SPalm GSchwenker F(2018)A Temporal Dependency Based Multi-modal Active Learning Approach for Audiovisual Event DetectionNeural Processing Letters10.5555/3288065.328813948:2(709-732)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288065.3288139
Callaghan WGoh JMohareb MLim ALaw E(2018)MechanicalHeartProceedings of the ACM on Human-Computer Interaction10.1145/32742972:CSCW(1-17)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3274297

Active learning applied to patient-adaptive heartbeat classification
1. Applied computing
  1. Life and medical sciences
  2. Operations research
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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cover image Guide Proceedings

NIPS'10: Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 2

December 2010

2630 pages

Publisher

Curran Associates Inc.

Red Hook, NY, United States

Publication History

Published: 06 December 2010

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Cited By

Thiam PMeudt SPalm GSchwenker F(2018)A Temporal Dependency Based Multi-modal Active Learning Approach for Audiovisual Event DetectionNeural Processing Letters10.5555/3288065.328813948:2(709-732)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288065.3288139
Callaghan WGoh JMohareb MLim ALaw E(2018)MechanicalHeartProceedings of the ACM on Human-Computer Interaction10.1145/32742972:CSCW(1-17)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3274297

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