Detail publikace

Taming of the monitors: reducing false alarms in intensive care units

PLEŠINGER, F. KLIMEŠ, P. HALÁMEK, J. JURÁK, P.

Originální název

Taming of the monitors: reducing false alarms in intensive care units

Český název

Taming of the monitors: reducing false alarms in intensive care units

Anglický název

Taming of the monitors: reducing false alarms in intensive care units

Typ

článek v časopise

Jazyk

en

Originální abstrakt

False alarms in intensive care units represent a serious threat to patients. We propose a method for detection of five live-threatening arrhythmias. It is designed to work with multimodal data containing electrocardiograph and arterial blood pressure or photoplethysmograph signals. The presented method is based on descriptive statistics and Fourier and Hilbert transforms. It was trained using 750 records. The method was validated during the follow-up phase of the CinC/Physionet Challenge 2015 on a hidden dataset with 500 records, achieving a sensitivity of 93% (95%) and a specificity of 87% (88%) for real-time (retrospective) files. The given sensitivity and specificity resulted in score of 81.62 (84.96) for real-time (retrospective) records. The presented method is an improved version of the original algorithm awarded the first and the second prize in CinC/Physionet Challenge 2015.

Český abstrakt

False alarms in intensive care units represent a serious threat to patients. We propose a method for detection of five live-threatening arrhythmias. It is designed to work with multimodal data containing electrocardiograph and arterial blood pressure or photoplethysmograph signals. The presented method is based on descriptive statistics and Fourier and Hilbert transforms. It was trained using 750 records. The method was validated during the follow-up phase of the CinC/Physionet Challenge 2015 on a hidden dataset with 500 records, achieving a sensitivity of 93% (95%) and a specificity of 87% (88%) for real-time (retrospective) files. The given sensitivity and specificity resulted in score of 81.62 (84.96) for real-time (retrospective) records. The presented method is an improved version of the original algorithm awarded the first and the second prize in CinC/Physionet Challenge 2015.

Anglický abstrakt

False alarms in intensive care units represent a serious threat to patients. We propose a method for detection of five live-threatening arrhythmias. It is designed to work with multimodal data containing electrocardiograph and arterial blood pressure or photoplethysmograph signals. The presented method is based on descriptive statistics and Fourier and Hilbert transforms. It was trained using 750 records. The method was validated during the follow-up phase of the CinC/Physionet Challenge 2015 on a hidden dataset with 500 records, achieving a sensitivity of 93% (95%) and a specificity of 87% (88%) for real-time (retrospective) files. The given sensitivity and specificity resulted in score of 81.62 (84.96) for real-time (retrospective) records. The presented method is an improved version of the original algorithm awarded the first and the second prize in CinC/Physionet Challenge 2015.

Klíčová slova

Arrhythmia * ECG * QRS * Detection * ICU

Vydáno

01.08.2016

Nakladatel

Physiological Measurement

Strany od

1313

Strany do

1325

Strany počet

12

URL

BibTex


@article{BUT127335,
  author="Filip {Plešinger} and Petr {Klimeš} and Josef {Halámek} and Pavel {Jurák}",
  title="Taming of the monitors: reducing false alarms in intensive care units",
  annote="False alarms in intensive care units represent a serious threat to patients. We propose a method for detection of five live-threatening arrhythmias. It is designed to work with multimodal data containing electrocardiograph and arterial blood pressure or photoplethysmograph signals. The presented method is based on descriptive statistics and Fourier and Hilbert transforms. It was trained using 750 records. The method was validated during the follow-up phase of the CinC/Physionet Challenge 2015 on a hidden dataset with 500 records, achieving a sensitivity of 93% (95%) and a specificity of 87% (88%) for real-time (retrospective) files. The given sensitivity and specificity resulted in score of 81.62 (84.96) for real-time (retrospective) records. The presented method is an improved version of the original algorithm awarded the first and the second prize in CinC/Physionet Challenge 2015.",
  address="Physiological Measurement",
  chapter="127335",
  doi="10.1088/0967-3334/37/8/1313",
  howpublished="online",
  institution="Physiological Measurement",
  number="8",
  volume="37",
  year="2016",
  month="august",
  pages="1313--1325",
  publisher="Physiological Measurement",
  type="journal article"
}