Deep neural network valve detection for accelerometer based cardiac monitoring.

Miniaturized accelerometers incorporated in pacing leads attached directly onto the heart provide a means for continuous monitoring of cardiac function. Several functional accelerometer indices first require detection of valve events. We previously developed a deep neural network to detect timing of aortic valve closure and opening. In this study we trained and tested the performance of the network to detect timing of mitral valve closure (MVC) and opening (MVO). Furthermore, we extracted four different functional indices based on the detected valve events and investigated how these indices reflected changes in cardiac function. The neural network was tested on approximately 5900 heartbeats from 289 recordings in a total of 46 animals with a cardiac accelerometer attached to the heart during various interventions that altered function. The neural network correctly detected MVO and MVC in 89.6% and 87.5% of the beats, respectively, with a mean absolute error of 13 ms between the detected values and the annotated targets for both. The functional indices correlated well with measured left ventricular stroke work (0.67 < r < 0.84) and showed expected changes for the different interventions. Hence, automatic detection of valve events is feasible and facilitates improved cardiac monitoring when using implanted cardiac accelerometers.