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.
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, who 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