Detecting instabilities of cardiac rhythm.

Area of Science:

• Cardiology
• Computational Biology
• Physiology

Background:

• Diminished beat-to-beat variations in cardiac cycle lengths (CLs) are linked to poor prognosis in myocardial infarction and heart failure.
• Ultra-short rhythm instabilities may precede ventricular tachyarrhythmias, but the significance of abrupt short-term instabilities in CL (AICL) hours before events is unclear due to analytical limitations.

Purpose of the Study:

• To compare analytical methods for detecting and quantifying abrupt short-term instabilities in cardiac cycle lengths (AICL).
• To investigate the clinical and prognostic significance of AICL preceding cardiac arrhythmias.

Main Methods:

• Compared time domain, spectral, nonlinear, and pattern recognition techniques for AICL analysis.
• Utilized continuous ambulatory ECG recordings and a mouse model of heart failure.
• Employed a two-dimensional cardiac tissue model (Morris-Lecar equations) and time-frequency analysis.

Main Results:

• Pattern recognition techniques showed superior performance in detecting and quantifying AICL due to high variability in CL.
• AICL were observed hours before spontaneous atrial and ventricular arrhythmias in patients and in a heart failure mouse model.
• AICL quantification using unstable orthogonal projection coefficients increased hours before events; removal of ectopic beats partially reduced AICL.
• Simulations suggested AICL can lead to wavefront irregularities and reentry initiation.
• AICL indicated autonomic nervous system maladaptation during head-up tilt tests.

Conclusions:

• Abrupt short-term instabilities in cardiac cycle lengths (AICL) can serve as a predictor of arrhythmias hours in advance.
• Pattern recognition offers a robust method for AICL analysis, aiding in the prediction of cardiac events.
• AICL may reflect underlying physiological instabilities, including autonomic nervous system dysfunction.