Challenge Papers: Reducing False Arrhythmia Alarms in the ICU

The papers below were presented at Computing in Cardiology 2015. Please cite this publication when referencing any of these papers. These papers have been made available by their authors under the terms of the Creative Commons Attribution License 3.0 (CCAL). We wish to thank all of the authors for their contributions.

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The first of these papers is an introduction to the challenge topic, with a summary of the challenge results and a discussion of their implications.

• The PhysioNet/Computing in Cardiology Challenge 2015: Reducing False Arrhythmia Alarms in the ICU
  Gari Clifford, Ikaro Silva, Benjamin Moody, Qiao Li, Danesh Kella, Abdullah Shahin, Tristan Kooistra, Diane Perry, Roger Mark

The remaining papers were presented by participants in the Challenge, ho describe their approaches to the challenge problem.

• Identification of ECG Signal Pattern Changes to Reduce the Incidence of Ventricular Tachycardia False Alarms
  Vytautas Abromavičius, Artūras Serackis, Andrius Gudiškis
• Multi-modal Integrated Approach towards Reducing False Arrhythmia Alarms During Continuous Patient Monitoring: the PhysioNet Challenge 2015
  Sardar Ansari, Ashwin Belle, Kayvan Najarian
• Reduction of False Cardiac Arrhythmia Alarms Through the Use of Machine Learning Techniques
  Miguel Caballero, Grace Mirsky
• Suppression of False Arrhythmia Alarms Using ECG and Pulsatile Waveforms
  Paula Couto, Ruben Ramalho, Rui Rodrigues
• Heart Beat Fusion Algorithm to Reduce False Alarms for Arrhythmias
  Chathuri Daluwatte, Lars Johannesen, Jose Vicente, Christopher G. Scully, Loriano Galeotti, David G. Strauss
• Decreasing the False Alarm Rate of Arrhythmias in Intensive Care Using a Machine Learning Approach
  Linda M. Eerikäinen, Joaquin Vanschoren, Michael J. Rooijakkers, Rik Vullings, Ronald M. Aarts
• A Multimodal Approach to Reduce False Arrhythmia Alarms in the Intensive Care Unit
  Sibylle Fallet, Sasan Yazdani, Jean-Marc Vesin
• Algorithm for Life-Threatening Arrhythmias Detection with Reduced False Alarms Ratio
  Iga Grzegorczyk, Kamil Ciuchciński, Jan Gierałtowski, Katarzyna Kośna, Piotr Podziemski, Mateusz Soliński
• Reducing False Arrhythmia Alarms in the ICU Using Novel Signal Quality Indices Assessment Method
  Runnan He, Henggui Zhang, Kuanquan Wang, Yongfeng Yuan, Qince Li, Jiabin Pan, Zhiqiang Sheng, Na Zhao
• Reducing False Arrhythmia Alarms Using Robust Interval Estimation and Machine Learning
  Christoph Hoog Antink, Steffen Leonhardt
• Enhancing Accuracy of Arrhythmia Classification by Combining Logical and Machine Learning Techniques
  Vignesh Kalidas, Lakshman Tamil
• Validation of Arrhythmia Detection Library on Bedside Monitor Data for Triggering Alarms in Intensive Care
  Vessela Krasteva, Irena Jekova, Remo Leber, Ramun Schmid, Roger Abaecherli
• Reduction of False Alarms in Intensive Care Unit using Multi-feature Fusion Method
  Chengyu Liu, Lina Zhao, Hong Tang
• False Alarms in Intensive Care Unit Monitors: Detection of Life-threatening Arrhythmias Using Elementary Algebra, Descriptive Statistics and Fuzzy Logic
  Filip Plesinger, Petr Klimes, Josef Halamek, Pavel Jurak
• Reducing False Arrhythmia Alarms in the ICU by Hilbert QRS Detection
  Nadi Sadr, Jacqueline Huvanandana, Doan Trang Nguyen, Chandan Kalra, Alistair McEwan, Philip de Chazal
• Reducing False Arrhythmia Alarms in the ICU
  Soo-Kng Teo, Jian Cheng Wong, Bo Yang, Feng Yang, Ling Feng, Toon Wei Lim, Yi Su
• Reliability of Clinical Alarm Detection in Intensive Care Units
  Charalampos Tsimenidis, Alan Murray
• Multimodal Data Classification Using Signal Quality Indices and Empirical Similarity-Based Reasoning
  Man Xu, Jiang Shen, Haiyan Yu
• Reduction of False Critical ECG Alarms using Waveform Features of Arterial Blood Pressure and/or Photoplethysmogram Signals
  Wei Zong

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Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362.

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