Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Abstract

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based analysis of 12-lead electrocardiograms (ECGs) can already detect arrhythmias and hypertension. We performed an observational two-center study in order to develop a machine learning algorithm to proactively detect arterial hypertension from single-lead ECGs. This could serve as proof of concept with an eye towards todays wearables that record single-lead ECGs. In a prospective observational study, we enrolled 1254 consecutive subjects (539 male, aged 60.22 ± 12.46 years), with and without essential hypertension, and no indications of CVD. A 12-lead ECG of 10 seconds duration in resting position was performed on each subject using a digital electrocardiograph and lead I was isolated for analysis using a calibrated Random Forest (RF). Our RF model classified hypertensive from normotensive subjects on a hold-out test set, with 75% accuracy, ROC/AUC 0.831 (95%CI: 0.781-0.871), sensitivity 72%, and specificity 82% (sensitivity and specificity calculated using a threshold of 0.675). Increasing age, larger values of body mass index, the area under the T wave divided by the QRS complex area, and the area under QRS segment adjusted for BMI, were the four most important features that drove the classification decisions of our model. This study demonstrates the potential to opportunistically detect an undiagnosed hypertension, using a single-lead ECG. While studies with data from wearables are required to translate our findings to actual smartwatch settings, our results could pave the way to innovative technologies for hypertension awareness.