A class of Monte-Carlo-based statistical algorithms for efficient detection of repolarization alternans

Area of Science:

• Cardiology
• Biophysics
• Computational Biology

Background:

• Cardiac repolarization alternans, characterized by beat-to-beat action potential waveform fluctuations, is linked to T-wave alternans, cardiac reentry, and sudden cardiac death.
• Existing detection algorithms are often tailored for T-wave alternans and may not be optimal for experimental action potential duration (APD) signals, particularly from optical mapping.

Purpose of the Study:

• To develop and present a new class of algorithms for the detection and quantitative measurement of cardiac alternans.
• To address the limitations of current methods when applied to experimental APD signals with unique characteristics.

Main Methods:

• Development of novel algorithms based on the Monte Carlo method for alternans detection.
• Derivation of both analytical and efficient versions of the proposed algorithms.
• Comparison of the new algorithms against standard spectral methods and the generalized likelihood ratio test using synthetic and experimental optical mapping data.

Main Results:

• The proposed Monte Carlo-based algorithms demonstrate superior performance in detecting alternans.
• The new algorithms show robustness in the presence of various noise types (Gaussian, Laplacian) and frame-shift errors.
• The algorithms exhibit low complexity and are suitable for fixed-point arithmetic implementation.

Conclusions:

• The developed Monte Carlo algorithms provide an effective and accurate method for detecting and quantifying cardiac repolarization alternans from experimental APD signals.
• These algorithms are well-suited for experimental applications and hold potential for integration into implantable cardiac devices due to their efficiency and low complexity.