Machine learning of microvolt-level 12-lead electrocardiogram can help distinguish takotsubo syndrome and acute anterior myocardial infarction.

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

SUBMITTER: Shimizu M 

PROVIDER: S-EPMC9422059 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature