Machine learning-powered, device-embedded heart sound measurement can optimize AV delay in patients with CRT.

Background: Continuous optimization of atrioventricular (AV) delay for cardiac resynchronization therapy (CRT) is mainly performed by electrical means.

Objective: The purpose of this study was to develop an estimation model of cardiac function that uses a piezoelectric microphone embedded in a pulse generator to guide CRT optimization.

Methods: Electrocardiogram, left ventricular pressure (LVP), and heart sounds were simultaneously collected during CRT device implantation procedures. A piezoelectric alarm transducer embedded in a modified CRT device facilitated recording of heart sounds in patients undergoing a pacing protocol with different AV delays. Machine learning (ML) was used to produce a decision-tree ensemble model capable of estimating absolute maximal LVP (LVP) and maximal rise of LVP (LVdP/dt) using 3 heart sound-based features. To gauge the applicability of ML in AV delay optimization, polynomial curves were fitted to measured and estimated values.

Results: In the data set of ∼30,000 heartbeats, ML indicated S1 amplitude, S2 amplitude, and S1 integral (S1 energy for LVdP/dt) as most prominent features for AV delay optimization. ML resulted in single-beat estimation precision for absolute values of LVP and LVdP/dt of 67% and 64%, respectively. For 20-30 beat averages, cross-correlation between measured and estimated LVP and LVdP/dt was 0.999 for both. The estimated optimal AV delays were not significantly different from those measured using invasive LVP (difference -5.6 ± 17.1 ms for LVP and +5.1 ± 6.7 ms for LVdP/dt). The difference in function at estimated and measured optimal AV delays was not statiscally significant (1 ± 3 mm Hg for LVP and 9 ± 57 mm Hg/s for LVdP/dt).

Conclusion: Heart sound sensors embedded in a CRT device, powered by a ML algorithm, provide a reliable assessment of optimal AV delays and absolute LVP and LVdP/dt.