Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory Environments
An algorithm to detect poor quality ECGs collected in low-resource environments is described (and was entered in the PhysioNet/Computing in Cardiology Challenge 2011 'Improving the quality of ECGs collected using mobile phones'). The algorithm is based on previously published signal qualit...
| Main Authors: | , , , , |
|---|---|
| 格式: | Journal article |
| 語言: | English |
| 出版: |
2011
|
| _version_ | 1826304304357572608 |
|---|---|
| author | Clifford, G Lopez, D Li, Q Rezek, I IEEE |
| author_facet | Clifford, G Lopez, D Li, Q Rezek, I IEEE |
| author_sort | Clifford, G |
| collection | OXFORD |
| description | An algorithm to detect poor quality ECGs collected in low-resource environments is described (and was entered in the PhysioNet/Computing in Cardiology Challenge 2011 'Improving the quality of ECGs collected using mobile phones'). The algorithm is based on previously published signal quality metrics, with some novel additions, designed for intensive care monitoring. The algorithms have been adapted for use on short (10s) 12-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Six metrics are produced for each channel (72 features in all) and presented to machine learning algorithms for training on the provided labeled data (Set-a) for the challenge. (Binary labels were available, indicating whether the data were acceptable or unacceptable for clinical interpretation.) We re-annotated all the data in Set-a as well as those in Set-b (the test data) using two independent annotators, and a third for adjudication of differences. Events were balanced and the 1000 subjects in Set-a were used to train the classifiers. We compared four classifiers: Linear Discriminant Analysis, Naïve Bayes, a Support Vector Machine (SVM) and a Multi-Layer Perceptron artificial neural network classifiers. The SVM and MLP provided the best (and almost equivalent) classification accuracies of 99% on the training data (Set-a) and 95% on the test data (Set-b). The binary classification results (acceptable or unacceptable) were then submitted as an entry into the PhysioNet Computing in Cardiology Competition 2011. Before the competition deadline, we scored 92.6% on the unseen test data (0.6% less than the winning entry). After improving labelling inconsistencies and errors we achieved 94.0%, the highest overall score of all competition entrants. © 2011 CCAL. |
| first_indexed | 2024-03-07T06:15:47Z |
| format | Journal article |
| id | oxford-uuid:f109739c-778f-4ea6-85f8-a4703a4aff49 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:15:47Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:f109739c-778f-4ea6-85f8-a4703a4aff492022-03-27T11:52:48ZSignal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory EnvironmentsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f109739c-778f-4ea6-85f8-a4703a4aff49EnglishSymplectic Elements at Oxford2011Clifford, GLopez, DLi, QRezek, IIEEEAn algorithm to detect poor quality ECGs collected in low-resource environments is described (and was entered in the PhysioNet/Computing in Cardiology Challenge 2011 'Improving the quality of ECGs collected using mobile phones'). The algorithm is based on previously published signal quality metrics, with some novel additions, designed for intensive care monitoring. The algorithms have been adapted for use on short (10s) 12-lead ECGs. The metrics quantify spectral energy distribution, higher order moments and inter-channel and inter-algorithm agreement. Six metrics are produced for each channel (72 features in all) and presented to machine learning algorithms for training on the provided labeled data (Set-a) for the challenge. (Binary labels were available, indicating whether the data were acceptable or unacceptable for clinical interpretation.) We re-annotated all the data in Set-a as well as those in Set-b (the test data) using two independent annotators, and a third for adjudication of differences. Events were balanced and the 1000 subjects in Set-a were used to train the classifiers. We compared four classifiers: Linear Discriminant Analysis, Naïve Bayes, a Support Vector Machine (SVM) and a Multi-Layer Perceptron artificial neural network classifiers. The SVM and MLP provided the best (and almost equivalent) classification accuracies of 99% on the training data (Set-a) and 95% on the test data (Set-b). The binary classification results (acceptable or unacceptable) were then submitted as an entry into the PhysioNet Computing in Cardiology Competition 2011. Before the competition deadline, we scored 92.6% on the unseen test data (0.6% less than the winning entry). After improving labelling inconsistencies and errors we achieved 94.0%, the highest overall score of all competition entrants. © 2011 CCAL. |
| spellingShingle | Clifford, G Lopez, D Li, Q Rezek, I IEEE Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory Environments |
| title | Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory Environments |
| title_full | Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory Environments |
| title_fullStr | Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory Environments |
| title_full_unstemmed | Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory Environments |
| title_short | Signal Quality Indices and Data Fusion for Determining Acceptability of Electrocardiograms Collected in Noisy Ambulatory Environments |
| title_sort | signal quality indices and data fusion for determining acceptability of electrocardiograms collected in noisy ambulatory environments |
| work_keys_str_mv | AT cliffordg signalqualityindicesanddatafusionfordeterminingacceptabilityofelectrocardiogramscollectedinnoisyambulatoryenvironments AT lopezd signalqualityindicesanddatafusionfordeterminingacceptabilityofelectrocardiogramscollectedinnoisyambulatoryenvironments AT liq signalqualityindicesanddatafusionfordeterminingacceptabilityofelectrocardiogramscollectedinnoisyambulatoryenvironments AT rezeki signalqualityindicesanddatafusionfordeterminingacceptabilityofelectrocardiogramscollectedinnoisyambulatoryenvironments AT ieee signalqualityindicesanddatafusionfordeterminingacceptabilityofelectrocardiogramscollectedinnoisyambulatoryenvironments |