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.