Localizing Placement of Cardiomechanical Sensors during Dynamic Periods via Template Matching

Captured with a chest-mounted sensor, the seismo- cardiogram (SCG) is a useful signal for assessing cardiomechanical function. However, the reliability of information obtained from this signal often depends upon sensor location. This has important practical implications, as consistent placement is not guaranteed in at-home and other uncontrolled settings. Building on prior research that localized SCG sensor placement when the patient was at rest — which may not be the case in practical settings — this work presents a more robust method which is able to localize sensor placement during dynamic periods, specifically exercise recovery. This was accomplished via a template-based signal quality index (SQI), which was used to infer sensor location using a variety of classifiers. While prior work generated synthetic templates for this task using an averaging method, it is shown that selecting representative templates from the training set instead enables, for the first time, SCG sensor localization during dynamic periods without patient-specific calibration. With this method, a peak accuracy of 83.32% was achieved for correctly classifying sensor position among five tested positions, with avenues for improvement of these results also presented.