Vocalization removal for improved automatic segmentation of dual-axis swallowing accelerometry signals

Automatic segmentation of dual-axis swallowing accelerometry signals can be severely affected by strong vocalizations. In this paper, a method based on periodicity detection is proposed to detect and remove such vocalizations. Periodic signal components are detected using conventional speech processing techniques and information from both axes are combined to improve vocalization detection accuracy. Experiments with 408 healthy subjects performing dry, wet, and wet chin tuck swallows show that the proposed method attains an average 95.3% sensitivity and 96.3% specificity. When applied in conjunction with an automatic segmentation algorithm, it is observed that segmentation accuracy improves by approximately 55%. These results encourage further development of medical devices for the detection of swallowing difficulties.