Advanced signal-processing method for the detection, localization, and quantification of acute myocardial ischemia

Area of Science:

• Cardiology
• Biomedical Engineering
• Signal Processing

Background:

• Myocardial ischemia detection is crucial for timely intervention.
• Current methods may have limitations in real-time localization and quantification.
• Electrocardiogram (ECG) analysis offers a non-invasive approach to assess cardiac health.

Purpose of the Study:

• To develop and validate a computer-based signal processing method for detecting and localizing epicardial projections of ischemic myocardial ECGs.
• To correlate ECG spectral parameters with myocardial metabolic markers.
• To assess the potential of this method for quantifying reversible myocardial ischemia.

Main Methods:

• A canine heart model of localized reversible ischemia was used.
• ECG signals from an epicardial sensor array were transformed into the multichannel spectral domain.
• Three decision variables (f0, w0, e0) were identified and analyzed using Neyman-Pearson tests and regression analysis.

Main Results:

• The method successfully detected and localized myocardial ischemia.
• Decreased f0 and w0, and increased e0 correlated with increased myocardial lactate and decreased ATP and creatine phosphate.
• The f0 parameter directly reflected myocardial energetics, correlating with ATP and creatine phosphate levels.

Conclusions:

• An advanced signal-processing method for ECG array data can detect, localize, and quantify reversible myocardial ischemia.
• This technique provides insights into myocardial energetics and metabolic changes during ischemia.
• The developed method shows promise for improved diagnosis and management of ischemic heart conditions.