ArticleCardiovascular digital health journal2022
Machine learning of microvolt-level 12-lead electrocardiogram can help distinguish takotsubo syndrome and acute anterior myocardial infarction.
Article in Cardiovascular digital health journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Takotsubo Syndrome in 2025: Evolving Concepts in Pathophysiology, Diagnosis, and Long-Term Management.Journal of clinical medicine · 2025Review
- Machine learning electrocardiography model to differentiate takotsubo syndrome from myocardial infarction.European heart journal. Digital health · 2025Article
- Risk prediction of inappropriate implantable cardioverter-defibrillator therapy using machine learning.Scientific reports · 2023Article
- Increasing averaging beats improves the test accuracy on Holter-based late potentials in patients with myocardial infarction.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- Diurnal Variation in and Optimal Time to Measure Holter-Based Late Potentials to Predict Lethal Arrhythmia after Myocardial Infarction.Medicina (Kaunas, Lithuania) · 2023Article
- Machine learning models in predicting health care costs in patients with a recent acute coronary syndrome: A prospective pilot study.Cardiovascular digital health journal · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Qualitative differences in 12-lead electrocardiograms (ECG) at onset have been reported in patients with takotsubo syndrome (TTS) and acute anterior myocardial infarction (Ant-AMI). We aimed to distinguish these diseases by machine learning (ML) approach of microvolt-level quantitative measurements. Methods: We enrolled 56 consecutive patients with sinus rhythm TTS (median age, 77 years; 16 men), and 1-to-1 random matching was performed based on age and sex of the patients. The ECG in the emergency room was evaluated using an automated system (ECAPs12c; Nihon-Koden). Statistical and ML predictive models for TTS were constructed using clinical features and ECG parameters. Results: Statistically significant differences were observed in 25 parameters; the V Conclusion: ML applied to automated microvolt-level ECG measurements showed the possibility of distinguishing between TTS and Ant-AMI, which may be a clinically useful ECG-based discriminator.
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