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Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning
Eleni Angelaki1,2, Georgios D Barmparis1,2, Konstantinos Fragkiadakis3
1Institute of Theoretical and Computational Physics, University of Crete, Heraklion, Greece.
Insights
Artificial intelligence can detect hypertension using single-lead ECGs, aiding early cardiovascular disease awareness. This machine learning model shows promise for wearable technology in proactive health monitoring.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Hypertension detection is vital for reducing cardiovascular disease (CVD) burden.
- Artificial intelligence (AI) analysis of electrocardiograms (ECGs) can identify arrhythmias and hypertension.
- Current AI ECG analysis primarily uses 12-lead ECGs, limiting widespread opportunistic screening.
Purpose of the Study:
- To develop a machine learning algorithm for proactive arterial hypertension detection using single-lead ECGs.
- To establish a proof of concept for hypertension detection in wearable devices.
- To investigate the feasibility of opportunistic hypertension screening via single-lead ECG analysis.
Main Methods:
- An observational, two-center study enrolled 1254 subjects (539 male, mean age 60.22 years) with and without essential hypertension.
- A 10-second, single-lead (Lead I) ECG was recorded from each subject using a digital electrocardiograph.
- A calibrated Random Forest (RF) model was developed and validated on a hold-out test set.
Main Results:
- The RF model achieved 75% accuracy in classifying hypertensive from normotensive subjects.
- The model demonstrated an ROC/AUC of 0.831, with 72% sensitivity and 82% specificity.
- Key features driving classification included age, body mass index (BMI), T wave area/QRS complex area, and BMI-adjusted QRS segment area.
Conclusions:
- Single-lead ECG analysis holds significant potential for opportunistic detection of undiagnosed hypertension.
- The findings support the development of innovative technologies for hypertension awareness, particularly in wearable settings.
- Further studies utilizing wearable device data are needed to translate these findings into practical applications.
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.
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