Post-Segmentation Swallowing Accelerometry Signal Trimming and False Positive Reduction

Abstract

Dual-axis swallowing accelerometry has been proposed as a method for quantifying swallowing function. Acceleration signals in anterior–posterior and superior–inferior anatomical directions are processed and automatically segmented. However, the latter is often too liberal, admitting pre- and post-swallowing activity while also giving rise to non-swallow segments. These segmentation shortcomings adversely affect feature extraction and ultimately classification of swallowing function. In this paper, we propose a kernel density estimation-based algorithm to adaptively trim the swallow segments, and energy and noise-floor algorithms to reduce the number of false positive swallow segments. The balance between false positive reduction and loss of true positives can be adjusted according to algorithmic thresholds. Dramatic reductions ( $-$ 85.4%) in false positives can be achieved with a moderate loss of true positives ( $-$ 15.1%).