ArticleOpen heart2023
Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data.
Article in Open heart, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Multimodal deep learning for acute myocardial infarction detection from 12-lead electrocardiogram: a multi-centre study with cross-hospital validation.European heart journal. Digital health · 2026Article
- Low-cost and convenient screening of disease using analysis of physical measurements and recordings.PLOS digital health · 2024Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputer-assisted interpretation of single-lead ECG is the preliminary method for clinicians to flag and further evaluate an arrhythmia of clinical importance for acutely ill patients. Critical scrutiny of novel detection algorithms is lacking, particularly in external real-world data sets. This study's objective was to evaluate a hybrid machine learning model's ability to classify eight arrhythmias from a single-lead ECG signal from acutely ill patients.
methodsThis cross-sectional external retrospective evaluation of a previously trained hybrid machine learning model against an ECG reading team in the setting of home hospital care (acute care delivered at home substituting for traditional hospital care) draws from patients admitted at two hospitals in Boston, Massachusetts, USA between 12 June 2017 and 23 November 2019. We calculated classifier statistics for each arrhythmia, all arrhythmias and strips where the model identified normal sinus rhythm.
resultsThe model analysed 2 680 162 min of single-lead ECG data from 423 patients and identified 691 478 arrhythmias. Patients had a mean age of 70 years (SD, 18), 60% were female and 45% were white. For any arrhythmia, the model had a sensitivity of 98%, a specificity of 100%, an accuracy of 98%, a positive predictive value of 100%, a negative predictive value of 93% and an F
conclusionsA hybrid machine learning model was effective at classifying common cardiac arrhythmias from a single-lead ECG in real-world data.
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