Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation
Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5618 |
| _version_ | 1797592650221617152 |
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| author | Markus Lueken Michael Gramlich Steffen Leonhardt Nikolaus Marx Matthias D. Zink |
| author_facet | Markus Lueken Michael Gramlich Steffen Leonhardt Nikolaus Marx Matthias D. Zink |
| author_sort | Markus Lueken |
| collection | DOAJ |
| description | Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications. |
| first_indexed | 2024-03-11T01:57:10Z |
| format | Article |
| id | doaj.art-8a51f8b925fc467fa8940d2414fac89b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:57:10Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8a51f8b925fc467fa8940d2414fac89b2023-11-18T12:33:41ZengMDPI AGSensors1424-82202023-06-012312561810.3390/s23125618Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial FibrillationMarkus Lueken0Michael Gramlich1Steffen Leonhardt2Nikolaus Marx3Matthias D. Zink4Medical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, GermanyDepartment of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, GermanyMedical Information Technology (MedIT), Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, GermanyDepartment of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, GermanyDepartment of Internal Medicine I-Cardiology, University Hospital RWTH, 52074 Aachen, GermanyAtrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.https://www.mdpi.com/1424-8220/23/12/5618atrial fibrillationECG signal quality assessmentsingle-lead ECG screening |
| spellingShingle | Markus Lueken Michael Gramlich Steffen Leonhardt Nikolaus Marx Matthias D. Zink Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation Sensors atrial fibrillation ECG signal quality assessment single-lead ECG screening |
| title | Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation |
| title_full | Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation |
| title_fullStr | Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation |
| title_full_unstemmed | Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation |
| title_short | Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation |
| title_sort | automated signal quality assessment of single lead ecg recordings for early detection of silent atrial fibrillation |
| topic | atrial fibrillation ECG signal quality assessment single-lead ECG screening |
| url | https://www.mdpi.com/1424-8220/23/12/5618 |
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