Wearable ECG Assessment of Ejection Fraction Without Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing left ventricular ejection fraction (LVEF) are costly, require specialized equipment, and are not easily accessible to patients in rural or underserved areas, limiting early diagnosis and affordable care for cardiovascular diseases.
Innovation Solution
A wearable electrocardiogram (ECG) device with LII and LIII leads, combined with software, captures and analyzes ECG data over an extended period to assess LVEF, heart failure status, and sleep apnea using artificial intelligence, providing a report on ejection fraction percentage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current modalities (echocardiography, radionuclide ventriculography, invasive left ventriculography, CT- or MRI-ventriculography) are used to assess ejection fraction, then measurement precision is improved, but device complexity and cost increase, and accessibility decreases
Solution Approach 1:
The patent uses a simplified wearable ECG device to capture electrical signals that serve as a proxy for more complex imaging modalities. The ECG device records cardiac electrical activity, and through AI analysis, derives ejection fraction information without requiring the complex physical imaging equipment of echocardiography or MRI. This copying approach allows accurate assessment through a less complex intermediary measurement.
Solution Approach 2:
The patent replaces mechanical and physical imaging systems (ultrasound transducers, radionuclide cameras, MRI magnets, CT X-ray sources) with an electrical measurement system. The wearable ECG device uses electrical signal capture and computational algorithms to assess ejection fraction, substituting the complex mechanical imaging infrastructure with a simpler electrical sensing and processing system.
2Measurement precision
If current modalities are used to evaluate left ventricular ejection fraction, then measurement precision is improved, but ease of operation and accessibility worsen due to requirement of specialized equipment and trained technicians
Solution Approach 1:
The system performs self-service through automated AI analysis of ECG data. The wearable device captures ECG signals continuously, and the integrated processing system automatically analyzes the data to assess ejection fraction without requiring external specialized technicians. The algorithm independently processes the electrical signals and generates diagnostic information, eliminating the need for trained echocardiographers or radiologists to perform and interpret complex imaging studies.
Solution Approach 2:
The wearable ECG device serves multiple functions: it continuously monitors cardiac electrical activity, detects arrhythmias, and assesses ejection fraction. This multi-functional approach allows a single simple device to replace multiple specialized procedures (echocardiography, Holter monitoring, clinical assessments), making comprehensive cardiac evaluation accessible without requiring patients to undergo multiple separate specialized tests.
3Measurement precision
If current modalities are used for serial examinations over short periods, then measurement precision is improved, but loss of time and cost increase
Solution Approach 1:
The wearable ECG device enables continuous monitoring of cardiac electrical activity over extended periods. Instead of requiring patients to return to a clinic for discrete serial echocardiography appointments, the device continuously captures ECG data in the patient's natural environment, providing ongoing assessment of ejection fraction and cardiac function. This continuous action eliminates travel time and scheduling delays associated with repeated clinic visits.
Solution Approach 2:
The system performs preliminary assessment and continuous monitoring in the patient's home environment before any clinical intervention is needed. By continuously capturing ECG data and analyzing it in real-time, the system detects changes in ejection fraction or cardiac function early, allowing timely clinical intervention without requiring frequent scheduled clinic appointments for serial examinations.
Data Source
AI summary
Described herein are systems and methods for characterizing a subject's ejection fraction (EF) status, heart failure status, and/or sleep apnea status. In particular, described herein are systems and methods for characterizing a subject's EF status, heart failure status, and/or sleep apnea status through use of a wearable electrocardiogram (ECG) device having LII and LIII leads, and software configured to incorporate data from the ECG device and assess the subject's EF status, heart failure status, and/or sleep apnea status.


