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Decision Tree Predictive Learner-Based Approach for False Alarm Detection in ICU

  • Image & Signal Processing
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Abstract

In this work, a novel method has been proposed for false alarm detection in Intensive Care Unit (ICU) during arrhythmia. To detect false alarm, various inputs are used such as electrocardiogram (ECG) signals, atrial blood pressure (ABP), photoplethysmogram signals (PLETH) and respiration (RESP). The inputs are given to decision tree predictive learner (DTPL) based classifier for thedetection of false alarm. The proposed method has an accuracy of 97% for prediction of false alarm in ICU. Theresult of the proposed method is promising which suggest that it can be used effectively for false alarm detection in ICUs. To the best of our knowledge, there is no such assumption based classification approach.

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Authors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, India

    Tishya Manna & Aleena Swetapadma

  2. Départementd’informatique, Université du Québec à Montréal, Montréal, Québec, Canada

    Moloud Abdar

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  1. Tishya Manna
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Correspondence to Aleena Swetapadma.

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Manna, T., Swetapadma, A. & Abdar, M. Decision Tree Predictive Learner-Based Approach for False Alarm Detection in ICU. J Med Syst 43, 191 (2019). https://doi.org/10.1007/s10916-019-1337-y

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