A Single Channel ECG Quality Metric

We describe a framework for automated electrocardiogram (ECG) quality assessment which works in both normal and arrhythmic situations, on an arbitrary number of ECG leads and for time periods of as short as five seconds. Originally developed for the Physionet/Computing in Cardiology (CinC) Challenge...

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Main Authors: Behar, J, Oster, J, Li, Q, Clifford, G
Format: Journal article
Language:English
Published: 2012
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author Behar, J
Oster, J
Li, Q
Clifford, G
author_facet Behar, J
Oster, J
Li, Q
Clifford, G
author_sort Behar, J
collection OXFORD
description We describe a framework for automated electrocardiogram (ECG) quality assessment which works in both normal and arrhythmic situations, on an arbitrary number of ECG leads and for time periods of as short as five seconds. Originally developed for the Physionet/Computing in Cardiology (CinC) Challenge 2011, we present here an extension to our original works with improved quality metrics. We manually annotated the 18000 single lead from the Challenge dataset as well as 9452, 10s segments (of both leads) from every subject in the MIT-BIH arrhythmia database as clinically acceptable or not. To balance the classes, noisy segments from the Noise Stress Test Database were added to clean data segments. A support vector machine was then trained to classify the data as clinically acceptable or not. A 97.1% accuracy was achieved on the test set of the extended database of 10s recordings, dropping almost linearly to 92.4% for 5s recordings. Retraining the classifier on all the challenge data, the classifier gave 93% accuracy on the MIT-BIH arrhythmia database. The results are promising and indicate that our method may be applied to Holter and intensive care unit monitoring. © 2012 CCAL.
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spelling oxford-uuid:a50ce436-7a2c-47fb-9f82-35f25d3fd6c62022-03-27T02:37:45ZA Single Channel ECG Quality MetricJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a50ce436-7a2c-47fb-9f82-35f25d3fd6c6EnglishSymplectic Elements at Oxford2012Behar, JOster, JLi, QClifford, GWe describe a framework for automated electrocardiogram (ECG) quality assessment which works in both normal and arrhythmic situations, on an arbitrary number of ECG leads and for time periods of as short as five seconds. Originally developed for the Physionet/Computing in Cardiology (CinC) Challenge 2011, we present here an extension to our original works with improved quality metrics. We manually annotated the 18000 single lead from the Challenge dataset as well as 9452, 10s segments (of both leads) from every subject in the MIT-BIH arrhythmia database as clinically acceptable or not. To balance the classes, noisy segments from the Noise Stress Test Database were added to clean data segments. A support vector machine was then trained to classify the data as clinically acceptable or not. A 97.1% accuracy was achieved on the test set of the extended database of 10s recordings, dropping almost linearly to 92.4% for 5s recordings. Retraining the classifier on all the challenge data, the classifier gave 93% accuracy on the MIT-BIH arrhythmia database. The results are promising and indicate that our method may be applied to Holter and intensive care unit monitoring. © 2012 CCAL.
spellingShingle Behar, J
Oster, J
Li, Q
Clifford, G
A Single Channel ECG Quality Metric
title A Single Channel ECG Quality Metric
title_full A Single Channel ECG Quality Metric
title_fullStr A Single Channel ECG Quality Metric
title_full_unstemmed A Single Channel ECG Quality Metric
title_short A Single Channel ECG Quality Metric
title_sort single channel ecg quality metric
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