ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Electrocardiogram (ECG) monitoring is crucial for diagnosing heart conditions.
• Traditional multi-lead ECG systems generate large data volumes, posing storage and transmission challenges.
• Compressed Sensing (CS) offers a potential solution for efficient signal acquisition.

Purpose of the Study:

• To develop and validate a novel dynamic Compressed Sensing (CS) method for multi-lead ECG monitoring.
• To adapt a single-lead CS technique for simultaneous compression of multiple ECG leads.
• To assess the performance of the proposed method across diverse cardiac conditions.

Main Methods:

• A dynamic CS method was extended from single-lead to multi-lead ECG signal acquisition.
• A single sensing matrix, derived from a combination of multiple leads, was utilized.
• The method was tested on ECG signals from healthy individuals and patients with various cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block).

Main Results:

• The proposed CS method achieved effective compression of multi-lead ECG signals.
• Signal quality was maintained at Compression Ratios (CR) up to 10.
• At CR=10, the average root-mean-squared difference across various ECG signals was below 3%.

Conclusions:

• The developed dynamic CS method is suitable for efficient multi-lead ECG monitoring.
• The technique offers a significant reduction in data requirements without compromising diagnostic accuracy.
• This approach has potential applications in remote patient monitoring and wearable ECG devices.