Left ventricular dysfunction discriminated noninvasively beat by beat

Area of Science:

• Cardiology
• Biomedical Engineering
• Noninvasive Diagnostics

Background:

• Left ventricular (LV) dysfunction detection is crucial for cardiac health.
• Noninvasive methods for identifying dysfunctional heartbeats are needed.
• Systolic time intervals (STI) and ballistocardiograms (BCG) offer potential diagnostic insights.

Purpose of the Study:

• To develop and validate a noninvasive method for discriminating between normal and dysfunctional left ventricular heartbeats.
• To assess the utility of systolic time intervals and ballistocardiograms in identifying cardiac dysfunction.
• To explore the relationship between respiratory cycles and cardiac mechanical function in healthy and diseased individuals.

Main Methods:

• Simultaneous recording of systolic time intervals (STI), acceleration ballistocardiograms (BCG), and respiratory cycles in healthy men and patients with myocardial infarction or hypertension.
• Analysis of 36 measurements per heartbeat, focusing on 9 primary variables identified through statistical methods.
• Application of a nonlinear quadratic discriminant function for beat-by-beat classification of heartbeats as 'normal' or 'coronary'.

Main Results:

• The ratio of preejection period to LV ejection time (ET) varied with respiration in normal men but not in those with cardiac conditions.
• A quadratic discriminant function achieved 87% accuracy in identifying normal heartbeats and 98% in identifying coronary heart disease (CHD) beats in a training group.
• Testing on older normal men and hypertensive men showed 89% and 67% 'normal' beat identification, respectively, with significant variability in 'coronary' beat detection across patient groups.

Conclusions:

• Noninvasive assessment using STI and BCG, analyzed with quadratic discriminant analysis, can effectively identify dysfunctional heartbeats.
• This beat-by-beat identification may serve as a sensitive and prognostically valuable index for cardiac health.
• The method demonstrates potential for noninvasive cardiac diagnostics, particularly in distinguishing between normal and pathological cardiac function.