Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk.

ABSTRACT:

Aims

Incorporation of sex in study design can lead to discoveries in medical research. Deep neural networks (DNNs) accurately predict sex based on the electrocardiogram (ECG) and we hypothesized that misclassification of sex is an important predictor for mortality. Therefore, we first developed and validated a DNN that classified sex based on the ECG and investigated the outcome. Second, we studied ECG drivers of DNN-classified sex and mortality.

Methods and results

A DNN was trained to classify sex based on 131 673 normal ECGs. The algorithm was validated on internal (68 500 ECGs) and external data sets (3303 and 4457 ECGs). The survival of sex (mis)classified groups was investigated using time-to-event analysis and sex-stratified mediation analysis of ECG features. The DNN

SUBMITTER: Siegersma KR 

PROVIDER: S-EPMC9707888 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature