Detection of false arrhythmia alarms with emphasis on ventricular tachycardia

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Our approach to detecting false arrhythmia alarms in the intensive care unit
breaks down into several tasks. It involves beat detection on different signals:
electrocardiogram, photoplethysmogram and arterial blood pressure. The
quality of each channel has to be estimated in order to evaluate the reliability
of obtained beat detections. The information about the heart rate from the
different channels must be integrated in order to find a final conclusion. Some
alarm types require particular detectors as is the case of ventricular fibrillation.
To identify false ventricular tachycardia alarms we needed to classify heart
beats as normal/ventricular. For that purpose we introduce a new feature, QRS
polarity type. This feature was important in order to reduce misclassification
of ventricular beats: there was an improvement in the ventricular tachycardia
alarm true positive rate from 69% to 81%. However, the true negative rate was
reduced from 95% to 69% and our global challenge score (real-time event)
dropped from 79.02 to 74.28.
Our challenge algorithm achieved the third best score in the 2015 PhysioNet/
CinC challenge event 1 (real time).
Original languageEnglish
Pages (from-to)1326–1339
Number of pages14
JournalPhysiological Measurement
Volume37
Issue number8
DOIs
Publication statusPublished - Aug 2016

Fingerprint

Blood pressure
Ventricular Tachycardia
Electrocardiography
Cardiac Arrhythmias
Detectors
Ventricular Fibrillation
Critical Care
Arterial Pressure
Heart Rate

Keywords

  • arrhythmia
  • ECG
  • ICU alarms
  • signal processing

Cite this

@article{0bddd3738b784745b79813c56a9077fb,
title = "Detection of false arrhythmia alarms with emphasis on ventricular tachycardia",
abstract = "Our approach to detecting false arrhythmia alarms in the intensive care unitbreaks down into several tasks. It involves beat detection on different signals:electrocardiogram, photoplethysmogram and arterial blood pressure. Thequality of each channel has to be estimated in order to evaluate the reliabilityof obtained beat detections. The information about the heart rate from thedifferent channels must be integrated in order to find a final conclusion. Somealarm types require particular detectors as is the case of ventricular fibrillation.To identify false ventricular tachycardia alarms we needed to classify heartbeats as normal/ventricular. For that purpose we introduce a new feature, QRSpolarity type. This feature was important in order to reduce misclassificationof ventricular beats: there was an improvement in the ventricular tachycardiaalarm true positive rate from 69{\%} to 81{\%}. However, the true negative rate wasreduced from 95{\%} to 69{\%} and our global challenge score (real-time event)dropped from 79.02 to 74.28.Our challenge algorithm achieved the third best score in the 2015 PhysioNet/CinC challenge event 1 (real time).",
keywords = "arrhythmia , ECG, ICU alarms, signal processing",
author = "Couto, {Maria Paula da Costa} and Rodrigues, {Rui Alberto Pimenta}",
note = "Sem PDF. Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) (UID/MAT/00297/2013)",
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T1 - Detection of false arrhythmia alarms with emphasis on ventricular tachycardia

AU - Couto, Maria Paula da Costa

AU - Rodrigues, Rui Alberto Pimenta

N1 - Sem PDF. Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) (UID/MAT/00297/2013)

PY - 2016/8

Y1 - 2016/8

N2 - Our approach to detecting false arrhythmia alarms in the intensive care unitbreaks down into several tasks. It involves beat detection on different signals:electrocardiogram, photoplethysmogram and arterial blood pressure. Thequality of each channel has to be estimated in order to evaluate the reliabilityof obtained beat detections. The information about the heart rate from thedifferent channels must be integrated in order to find a final conclusion. Somealarm types require particular detectors as is the case of ventricular fibrillation.To identify false ventricular tachycardia alarms we needed to classify heartbeats as normal/ventricular. For that purpose we introduce a new feature, QRSpolarity type. This feature was important in order to reduce misclassificationof ventricular beats: there was an improvement in the ventricular tachycardiaalarm true positive rate from 69% to 81%. However, the true negative rate wasreduced from 95% to 69% and our global challenge score (real-time event)dropped from 79.02 to 74.28.Our challenge algorithm achieved the third best score in the 2015 PhysioNet/CinC challenge event 1 (real time).

AB - Our approach to detecting false arrhythmia alarms in the intensive care unitbreaks down into several tasks. It involves beat detection on different signals:electrocardiogram, photoplethysmogram and arterial blood pressure. Thequality of each channel has to be estimated in order to evaluate the reliabilityof obtained beat detections. The information about the heart rate from thedifferent channels must be integrated in order to find a final conclusion. Somealarm types require particular detectors as is the case of ventricular fibrillation.To identify false ventricular tachycardia alarms we needed to classify heartbeats as normal/ventricular. For that purpose we introduce a new feature, QRSpolarity type. This feature was important in order to reduce misclassificationof ventricular beats: there was an improvement in the ventricular tachycardiaalarm true positive rate from 69% to 81%. However, the true negative rate wasreduced from 95% to 69% and our global challenge score (real-time event)dropped from 79.02 to 74.28.Our challenge algorithm achieved the third best score in the 2015 PhysioNet/CinC challenge event 1 (real time).

KW - arrhythmia

KW - ECG

KW - ICU alarms

KW - signal processing

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DO - doi:10.1088/0967-3334/37/8/1326

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JO - Physiological Measurement

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SN - 0967-3334

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