Automated diagnosis of atrial fibrillation in 24-hour Holter recording based on deep learning:a study with randomized and real-world data validation Article Swipe
Peng Zhang
,
Fan Lin
,
Fei Ma
,
Yuting Chen
,
Dao Wen Wang
,
Xiaoyun Yang
,
Qiang Li
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.08.25.21262591
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.08.25.21262591
Summary Background With the increasing demand for atrial fibrillation (AF) screening, clinicians spend a significant amount of time in identifying the AF signals from massive electrocardiogram (ECG) data in long-term dynamic ECG monitoring. In this study, we aim to reduce clinicians’ workload and promote AF screening by using artificial intelligence (AI) to automatically detect AF episodes and identify AF patients in 24 h Holter recording. Methods We used a total of 22 979 Holter recordings (24 h) from 22 757 adult patients and established accurate annotations for AF by cardiologists. First, a randomized clinical cohort of 3 000 recordings (1 500 AF and 1 500 non-AF) from 3000 patients recorded between April 2012 and May 2020 was collected and randomly divided into training, validation and test sets (10:1:4). Then, a deep-learning-based AI model was developed to automatically detect AF episode using RR intervals and was tested with the test set. Based on AF episode detection results, AF patients were automatically identified by using a criterion of at least one AF episode of 6 min or longer. Finally, the clinical effectiveness of the model was verified with an independent real-world test set including 19 979 recordings (1 006 AF and 18 973 non-AF) from 19 757 consecutive patients recorded between June 2020 and January 2021. Findings Our model achieved high performance for AF episode detection in both test sets (sensitivity: 0.992 and 0.972; specificity: 0.997 and 0.997, respectively). It also achieved high performance for AF patient identification in both test sets (sensitivity:0.993 and 0.994; specificity: 0.990 and 0.973, respectively). Moreover, it obtained superior and consistent performance in an external public database. Interpretation Our AI model can automatically identify AF in long-term ECG recording with high accuracy. This cost-effective strategy may promote AF screening by improving diagnostic effectiveness and reducing clinical workload. Research in context Evidence before this study We searched Google Scholar and PubMed for research articles on artificial intelligence-based diagnosis of atrial fibrillation (AF) published in English between Jan 1, 2016 and Aug 1, 2021, using the search terms “deep learning” OR “deep neural network” OR “machine learning” OR “artificial intelligence” AND “atrial fibrillation”. We found that most of the previous deep learning models in AF detection were trained and validated on benchmark datasets (such as the PhysioNet database, the Massachusetts Institute of Technology Beth Israel Hospital AF database or Long-Term AF database), in which there were less than 100 patients or the recordings contained only short ECG segments (30-60s). Our search did not identify any articles that explored deep neural networks for AF detection in large real-world dataset of 24 h Holter recording, nor did we find articles that can automatically identify patients with AF in 24 h Holter recording. Added value of this study First, long-term Holter monitoring is the main method of AF screening, however, most previous studies of automatic AF detection mainly tested on short ECG recordings. This work focused on 24 h Holter recording data and achieved high accuracy in detecting AF episodes. Second, AF episodes detection did not automatically transform to AF patient identification in 24 h Holter recording, since at present, there is no well-recognized criterion for automatically identifying AF patient. Therefore, we established a criterion to identify AF patients by use of at least one AF episode of 6 min or longer, as this condition led to significantly increased risk of thromboembolism. Using this criterion, our method identified AF patients with high accuracy. Finally, and more importantly, our model was trained on a randomized clinical dataset and tested on an independent real-world clinical dataset to show great potential in clinical application. We did not exclude rare or special cases in the real-world dataset so as not to inflate our AF detection performance. To the best of our knowledge, this is the first study to automatically identifies both AF episodes and AF patients in 24 h Holter recording of large real-world clinical dataset. Implications of all the available evidence Our deep learning model automatically identified AF patient with high accuracy in 24 h Holter recording and was verified in real-world data, therefore, it can be embedded into the Holter analysis system and deployed at the clinical level to assist the decision making of Holter analysis system and clinicians. This approach can help improve the efficiency of AF screening and reduce the cost for AF diagnosis. In addition, our RR-interval-based model achieved comparable or better performance than the raw-ECG-based method, and can be widely applied to medical devices that can collect heartbeat information, including not only the multi-lead and single-lead Holter devices, but also other wearable devices that can reliably measure the heartbeat signals.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.08.25.21262591
- https://www.medrxiv.org/content/medrxiv/early/2021/08/29/2021.08.25.21262591.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198462784
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Raw OpenAlex JSON
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https://doi.org/10.1101/2021.08.25.21262591Digital Object Identifier
- Title
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Automated diagnosis of atrial fibrillation in 24-hour Holter recording based on deep learning:a study with randomized and real-world data validationWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-08-29Full publication date if available
- Authors
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Peng Zhang, Fan Lin, Fei Ma, Yuting Chen, Dao Wen Wang, Xiaoyun Yang, Qiang LiList of authors in order
- Landing page
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https://doi.org/10.1101/2021.08.25.21262591Publisher landing page
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https://www.medrxiv.org/content/medrxiv/early/2021/08/29/2021.08.25.21262591.full.pdfDirect link to full text PDF
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greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2021/08/29/2021.08.25.21262591.full.pdfDirect OA link when available
- Concepts
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Atrial fibrillation, Medicine, Test set, Internal medicine, Deep learning, Test (biology), Cohort, Artificial intelligence, Machine learning, Cardiology, Computer science, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
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2025: 1, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.detecting | 495 |
| abstract_inverted_index.detection | 154, 223, 366, 424, 474, 501, 618 |
| abstract_inverted_index.developed | 134 |
| abstract_inverted_index.diagnosis | 319 |
| abstract_inverted_index.episodes. | 497 |
| abstract_inverted_index.heartbeat | 747, 768 |
| abstract_inverted_index.improving | 293 |
| abstract_inverted_index.including | 191, 749 |
| abstract_inverted_index.increased | 556 |
| abstract_inverted_index.intervals | 142 |
| abstract_inverted_index.long-term | 29, 279, 457 |
| abstract_inverted_index.potential | 595 |
| abstract_inverted_index.published | 324 |
| abstract_inverted_index.recording | 281, 488, 644, 671 |
| abstract_inverted_index.screening | 45, 291, 714 |
| abstract_inverted_index.training, | 122 |
| abstract_inverted_index.transform | 505 |
| abstract_inverted_index.validated | 370 |
| abstract_inverted_index.workload. | 299 |
| abstract_inverted_index.“atrial | 352 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Technology | 383 |
| abstract_inverted_index.Therefore, | 528 |
| abstract_inverted_index.artificial | 48, 317 |
| abstract_inverted_index.clinicians | 11 |
| abstract_inverted_index.comparable | 728 |
| abstract_inverted_index.consistent | 263 |
| abstract_inverted_index.criterion, | 562 |
| abstract_inverted_index.database), | 392 |
| abstract_inverted_index.diagnosis. | 721 |
| abstract_inverted_index.diagnostic | 294 |
| abstract_inverted_index.efficiency | 711 |
| abstract_inverted_index.identified | 160, 565, 661 |
| abstract_inverted_index.identifies | 633 |
| abstract_inverted_index.increasing | 4 |
| abstract_inverted_index.knowledge, | 625 |
| abstract_inverted_index.monitoring | 459 |
| abstract_inverted_index.multi-lead | 753 |
| abstract_inverted_index.network” | 344 |
| abstract_inverted_index.randomized | 92, 581 |
| abstract_inverted_index.real-world | 188, 427, 589, 609, 647, 676 |
| abstract_inverted_index.recording, | 433, 514 |
| abstract_inverted_index.recording. | 64, 450 |
| abstract_inverted_index.recordings | 74, 98, 194, 403 |
| abstract_inverted_index.screening, | 10, 466 |
| abstract_inverted_index.therefore, | 678 |
| abstract_inverted_index.validation | 123 |
| abstract_inverted_index.“machine | 346 |
| abstract_inverted_index.annotations | 85 |
| abstract_inverted_index.clinicians. | 704 |
| abstract_inverted_index.consecutive | 205 |
| abstract_inverted_index.established | 83, 530 |
| abstract_inverted_index.identifying | 19, 525 |
| abstract_inverted_index.independent | 187, 588 |
| abstract_inverted_index.learning” | 340, 347 |
| abstract_inverted_index.monitoring. | 32 |
| abstract_inverted_index.performance | 219, 241, 264, 731 |
| abstract_inverted_index.recordings. | 480 |
| abstract_inverted_index.significant | 14 |
| abstract_inverted_index.single-lead | 755 |
| abstract_inverted_index.Implications | 650 |
| abstract_inverted_index.application. | 598 |
| abstract_inverted_index.fibrillation | 8, 322 |
| abstract_inverted_index.importantly, | 574 |
| abstract_inverted_index.information, | 748 |
| abstract_inverted_index.intelligence | 49 |
| abstract_inverted_index.performance. | 619 |
| abstract_inverted_index.specificity: | 232, 253 |
| abstract_inverted_index.(sensitivity: | 228 |
| abstract_inverted_index.Massachusetts | 380 |
| abstract_inverted_index.automatically | 52, 136, 159, 275, 441, 504, 524, 632, 660 |
| abstract_inverted_index.clinicians’ | 40 |
| abstract_inverted_index.effectiveness | 179, 295 |
| abstract_inverted_index.raw-ECG-based | 734 |
| abstract_inverted_index.significantly | 555 |
| abstract_inverted_index.“artificial | 349 |
| abstract_inverted_index.Interpretation | 270 |
| abstract_inverted_index.cardiologists. | 89 |
| abstract_inverted_index.cost-effective | 286 |
| abstract_inverted_index.identification | 245, 509 |
| abstract_inverted_index.respectively). | 236, 257 |
| abstract_inverted_index.intelligence” | 350 |
| abstract_inverted_index.well-recognized | 521 |
| abstract_inverted_index.fibrillation”. | 353 |
| abstract_inverted_index.thromboembolism. | 559 |
| abstract_inverted_index.RR-interval-based | 725 |
| abstract_inverted_index.electrocardiogram | 25 |
| abstract_inverted_index.(sensitivity:0.993 | 250 |
| abstract_inverted_index.intelligence-based | 318 |
| abstract_inverted_index.deep-learning-based | 130 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100430031, https://openalex.org/A5006828891 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I4210138186, https://openalex.org/I4210160344, https://openalex.org/I47720641 |
| citation_normalized_percentile.value | 0.53271405 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |