Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised Models Article Swipe
Ridwan Alam
,
Collin M. Stultz
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.12.24311879
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.08.12.24311879
The diagnosis, prognosis, and treatment of a number of cardiovascular disorders rely on ECG interval measurements, including the PR, QRS, and QT intervals. These quantities are measured from the 12-lead ECG, either manually or using automated algorithms, which are readily available in clinical settings. A number of wearable devices, however, can acquire the lead-I ECG in an outpatient setting, thereby raising the potential for out-of-hospital monitoring for disorders that involve clinically significant changes in ECG intervals. In this work, we therefore developed a series of deep learning models for estimating the PR, QRS, and QT intervals using lead-I ECG. From a corpus of 4.2 million ECGs from patients at the Massachusetts General Hospital, we train and validate each of the models. At internal holdout validation, we achieve mean absolute errors (MAE) of 6.3 ms for QRS durations and 11.9 ms for QT intervals, and an MAE of 9.2 ms for estimating PR intervals. Moreover, as a well-defined P-wave does not always exist in ECG tracings – for example, when there is atrial fibrillation – we trained a model that can identify when there is a P-wave, and consequently, a measurable PR interval. We validate our models on three large external healthcare datasets without any finetuning or retraining - 3.2 million ECG from the Brigham and Women’s Hospital, 668 thousand from MIMIC-IV, and 20 thousand from PTB-XL - and achieve similar performance. Also, our models significantly outperform two publicly available baseline algorithms. This work demonstrates that ECG intervals can be tracked from only lead-I ECG using deep learning, and highlights the potential for out-of-hospital applications.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.08.12.24311879
- https://www.medrxiv.org/content/medrxiv/early/2024/08/13/2024.08.12.24311879.full.pdf
- OA Status
- green
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401537071
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401537071Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.08.12.24311879Digital Object Identifier
- Title
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Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised ModelsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-13Full publication date if available
- Authors
-
Ridwan Alam, Collin M. StultzList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.08.12.24311879Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2024/08/13/2024.08.12.24311879.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2024/08/13/2024.08.12.24311879.full.pdfDirect OA link when available
- Concepts
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QRS complex, QT interval, Medicine, Lead (geology), Electrocardiography, Atrial fibrillation, Confidence interval, Interval (graph theory), Machine learning, Artificial intelligence, Computer science, Internal medicine, Mathematics, Geomorphology, Geology, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ECG | 14, 55, 75, 164, 211, 246, 254 |
| abstract_inverted_index.MAE | 146 |
| abstract_inverted_index.PR, | 19, 92 |
| abstract_inverted_index.QRS | 136 |
| abstract_inverted_index.The | 1 |
| abstract_inverted_index.and | 4, 21, 94, 116, 138, 144, 187, 215, 222, 228, 258 |
| abstract_inverted_index.any | 204 |
| abstract_inverted_index.are | 26, 39 |
| abstract_inverted_index.can | 51, 180, 248 |
| abstract_inverted_index.for | 64, 67, 89, 135, 141, 150, 167, 262 |
| abstract_inverted_index.not | 160 |
| abstract_inverted_index.our | 195, 233 |
| abstract_inverted_index.the | 18, 29, 53, 62, 91, 110, 120, 213, 260 |
| abstract_inverted_index.two | 237 |
| abstract_inverted_index.– | 166, 174 |
| abstract_inverted_index.11.9 | 139 |
| abstract_inverted_index.ECG, | 31 |
| abstract_inverted_index.ECG. | 99 |
| abstract_inverted_index.ECGs | 106 |
| abstract_inverted_index.From | 100 |
| abstract_inverted_index.QRS, | 20, 93 |
| abstract_inverted_index.This | 242 |
| abstract_inverted_index.deep | 86, 256 |
| abstract_inverted_index.does | 159 |
| abstract_inverted_index.each | 118 |
| abstract_inverted_index.from | 28, 107, 212, 220, 225, 251 |
| abstract_inverted_index.mean | 128 |
| abstract_inverted_index.only | 252 |
| abstract_inverted_index.rely | 12 |
| abstract_inverted_index.that | 69, 179, 245 |
| abstract_inverted_index.this | 78 |
| abstract_inverted_index.when | 169, 182 |
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| abstract_inverted_index.(MAE) | 131 |
| abstract_inverted_index.Also, | 232 |
| abstract_inverted_index.These | 24 |
| abstract_inverted_index.exist | 162 |
| abstract_inverted_index.large | 199 |
| abstract_inverted_index.model | 178 |
| abstract_inverted_index.there | 170, 183 |
| abstract_inverted_index.three | 198 |
| abstract_inverted_index.train | 115 |
| abstract_inverted_index.using | 35, 97, 255 |
| abstract_inverted_index.which | 38 |
| abstract_inverted_index.work, | 79 |
| abstract_inverted_index.P-wave | 158 |
| abstract_inverted_index.PTB-XL | 226 |
| abstract_inverted_index.always | 161 |
| abstract_inverted_index.atrial | 172 |
| abstract_inverted_index.corpus | 102 |
| abstract_inverted_index.either | 32 |
| abstract_inverted_index.errors | 130 |
| abstract_inverted_index.lead-I | 54, 98, 253 |
| abstract_inverted_index.models | 88, 196, 234 |
| abstract_inverted_index.number | 8, 46 |
| abstract_inverted_index.series | 84 |
| abstract_inverted_index.12-lead | 30 |
| abstract_inverted_index.Brigham | 214 |
| abstract_inverted_index.General | 112 |
| abstract_inverted_index.P-wave, | 186 |
| abstract_inverted_index.achieve | 127, 229 |
| abstract_inverted_index.acquire | 52 |
| abstract_inverted_index.changes | 73 |
| abstract_inverted_index.holdout | 124 |
| abstract_inverted_index.involve | 70 |
| abstract_inverted_index.million | 105, 210 |
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| abstract_inverted_index.without | 203 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.absolute | 129 |
| abstract_inverted_index.baseline | 240 |
| abstract_inverted_index.clinical | 43 |
| abstract_inverted_index.datasets | 202 |
| abstract_inverted_index.devices, | 49 |
| abstract_inverted_index.example, | 168 |
| abstract_inverted_index.external | 200 |
| abstract_inverted_index.however, | 50 |
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| abstract_inverted_index.internal | 123 |
| abstract_inverted_index.interval | 15 |
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| abstract_inverted_index.measured | 27 |
| abstract_inverted_index.patients | 108 |
| abstract_inverted_index.publicly | 238 |
| abstract_inverted_index.setting, | 59 |
| abstract_inverted_index.thousand | 219, 224 |
| abstract_inverted_index.tracings | 165 |
| abstract_inverted_index.validate | 117, 194 |
| abstract_inverted_index.wearable | 48 |
| abstract_inverted_index.Hospital, | 113, 217 |
| abstract_inverted_index.MIMIC-IV, | 221 |
| abstract_inverted_index.Moreover, | 154 |
| abstract_inverted_index.Women’s | 216 |
| abstract_inverted_index.automated | 36 |
| abstract_inverted_index.available | 41, 239 |
| abstract_inverted_index.developed | 82 |
| abstract_inverted_index.disorders | 11, 68 |
| abstract_inverted_index.durations | 137 |
| abstract_inverted_index.including | 17 |
| abstract_inverted_index.interval. | 192 |
| abstract_inverted_index.intervals | 96, 247 |
| abstract_inverted_index.learning, | 257 |
| abstract_inverted_index.potential | 63, 261 |
| abstract_inverted_index.settings. | 44 |
| abstract_inverted_index.therefore | 81 |
| abstract_inverted_index.treatment | 5 |
| abstract_inverted_index.clinically | 71 |
| abstract_inverted_index.diagnosis, | 2 |
| abstract_inverted_index.estimating | 90, 151 |
| abstract_inverted_index.finetuning | 205 |
| abstract_inverted_index.healthcare | 201 |
| abstract_inverted_index.highlights | 259 |
| abstract_inverted_index.intervals, | 143 |
| abstract_inverted_index.intervals. | 23, 76, 153 |
| abstract_inverted_index.measurable | 190 |
| abstract_inverted_index.monitoring | 66 |
| abstract_inverted_index.outpatient | 58 |
| abstract_inverted_index.outperform | 236 |
| abstract_inverted_index.prognosis, | 3 |
| abstract_inverted_index.quantities | 25 |
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| abstract_inverted_index.fibrillation | 173 |
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| abstract_inverted_index.significantly | 235 |
| abstract_inverted_index.cardiovascular | 10 |
| abstract_inverted_index.out-of-hospital | 65, 263 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5034393434 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I63966007 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.26282991 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |