Abstract
Previous studies have shown that miniaturised accelerometers can be used to monitor cardiac function and automatically detect ischemic events. However, accelerometers cannot differentiate between acceleration due to motion and acceleration due to gravity. Gravity filtering is essential for accurate integration of acceleration to yield velocity and displacement. Heart motion is cyclic and mean acceleration over time is zero. Thus, static gravity filtering is performed by subtracting mean acceleration. However, the heart rotates during the cycle, the gravity component is therefore not constant, resulting in overestimation of motion by static filtering. Accurate motion can be calculated using dynamic gravity filtering by a combined gyro and accelerometer. In an animal model, we investigated whether increased accuracy using dynamic filtering, compared to using static filtering, would enhance the ability to detect ischemia. Additionally, we investigated how well the gyro alone could detect ischemia based on the heart's rotation. Dynamic filtering tended towards lower sensitivity and specificity, using receiver operating characteristics analysis, for ischemia-detection compared to static filtering (area under the curve (AUC): 0.83 vs 0.93, p = 0.125). The time-varying gravity component indirectly reflects the heart's rotation. Hence, static filtering has the advantage of indirectly including rotation, which alone demonstrated excellent sensitivity to ischemia (AUC = 0.98).