Related Experiment Video
Updated: May 22, 2026
10:17
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
Optimizing single-lead ECG axis for AI-based detection of myocardial diseases
Masamitsu Nakayama1,2,3, Ryuichiro Yagi1,2, Kyohei Daigo4
1Division of Cardiovascular Medicine, Department of Medicine, Mass General Brigham, Boston, MA, USA.
NPJ Cardiovascular Health
|May 20, 2026
Summary
Wearable single-lead electrocardiograms (ECGs) can detect myocardial diseases like LVSD, HCM, and CA. Optimal lead angles near aVR improve detection accuracy beyond just rhythm abnormalities.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Wearable devices offer electrocardiogram (ECG) monitoring outside clinical settings.
- Single-lead ECGs typically detect rhythm issues, but AI integration shows potential for diagnosing myocardial diseases.
- The optimal lead axis for single-lead ECGs in diagnosing myocardial diseases is not well-defined.
Purpose of the Study:
- To determine the optimal single-lead ECG axis for detecting left ventricular systolic dysfunction (LVSD), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA).
- To evaluate the utility of AI-powered single-lead ECG analysis for myocardial disease detection beyond rhythm abnormalities.
Main Methods:
- Trained AI models using single-lead ECGs synthesized at 10-degree intervals from 12-lead ECG data.
- Systematically evaluated detection performance for LVSD, HCM, and CA across various synthesized lead angles.
- Utilized the area under the receiver operating characteristic curve (AUROC) to quantify diagnostic discrimination.
Main Results:
- Optimal detection for LVSD and HCM was achieved near the 20° axis (AUROC 0.884 and 0.911, respectively).
- Optimal detection for cardiac amyloidosis (CA) was achieved near the 210° axis (AUROC 0.893).
- Highest diagnostic discrimination for all three diseases was observed near the aVR lead axis and its inverse.
Conclusions:
- Specific single-lead ECG axes, particularly near aVR, can effectively detect myocardial diseases like LVSD, HCM, and CA.
- Findings support the development of advanced wearable ECG devices for diagnosing cardiac conditions beyond simple rhythm monitoring.
- AI-driven analysis of strategically positioned single-lead ECGs holds significant promise for non-invasive cardiac diagnostics.
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin to...
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin to...
01:29Electrocardiogram
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...
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