Automatic extraction of abnormal lip movement features from the alternating motion rate task in amyotrophic lateral sclerosis

Abstract

Purpose: With the long-term goal to develop a clinically feasible tool for assessing articulatory involvement in ALS, we designed an algorithmic approach to automatically extract lip movement features during an alternating motion rate (AMR) task and assessed their efficacy for detecting and monitoring articulatory involvement in amyotrophic lateral sclerosis (ALS).Method: Twenty three spatial, temporal, and spatiotemporal AMR features were extracted from 161 samples of lip movements (139 from participants with ALS; 22 from neurologically-intact participants). The diagnostic value of these features was assessed based on their (1) sensitivity for detecting early bulbar motor involvement, and (2) associations with accepted clinical measures of bulbar disease progression.Result: Among all AMR features, two temporal features were the most affected – temporal variability and syllable frequency, which (1) showed large changes during early disease stages and (2) predicted the progression of bulbar motor involvement and speech intelligibility decline. Spatial features were in general, less sensitive to early bulbar motor involvement.Conclusions: The findings provided preliminary support for the algorithmic approach to quantifying articulatory features predictive of bulbar motor and speech decline in ALS. The differential disease effects on spatial and temporal AMR features might shed light on the mechanism of articulatory involvement during ALS progression.