Smart Dysphagia Detection System with Adaptive Boosting Analysis of Throat Signals

Abstract

Dysphagia is a symptom of many neurological disorders. Existing diagnosis systems are either invasive or require swallowing liquids, which are costly and harmful to humans. In this work, we design a smart dysphagia detection system based on speech signals. Rather than the voice data acquired by traditional microphones, we apply a bone conduction headset for vibration signal acquisition from the throat to get cleaner speech signals.After speech feature extraction, under-sampling is performed to deal with the imbalanced data problem, and principal component analysis is used for dimensionality reduction. In this paper, we construct an ensemble adaptive boosting classifier to detect the dysphagia patient. Experimental results show that the testing classification accuracy of the proposed system reaches 71.2%. Sensitivity and specificity can reach 66.6 % and 76 %, respectively.

A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation

“…Monitoring and diagnosis of these behaviors can also prevent secondary injuries that often occur in patients with dysphagia disorders during normal daily activities. Currently, the commercial devices to track laryngeal signatures are rigid, bulky, and tethered, including the lip-closing force gauge 16 , non-invasive belts [17][18][19] , thin-film pressure sensors 20 , and others 21 . The poor device/skin contact also results in attenuated signals that are also susceptible to motion artifacts.…”