Methods for onset detection of voluntary motor responses in tremor patients

Accurate onset detection of the voluntary response is a prerequisite in reaction time studies when used in investigations on human motor control. The detection algorithm required performs a transformation of the continuous physical signal (e.g., force, movement) containing the response into a discrete event from which the reaction time (RT) is derived. Therefore, RT always comprises both the cognitive and/or motor delay component imaging the duration of the initiation process conducted by the sensorimotor system and, in addition, some spurious delay caused by the detection algorithm. As a standard method, onset detection is realized by the measurement device itself (e.g., the release of a micro switch) by defining the response onset as the point where the observed signal passes a certain threshold. Thus, weak and abnormal response profiles which are typical far a variety of central motor disorders (e.g., Parkinson's disease) may introduce high RT variability as well as systematic errors. The aim of this study was to improve accuracy of onset detection by application of an appropriate filter to the measured sid from dental radiographs, in detecting early simulated osteoporosis of alveolar bone. Five specimens of human maxillary alveolar bone were progressively decalcified and the percentage calcium lost at each decalcification stage quantified. Two radiographs of each specimen, together with an aluminum stepwedge, were exposed at 70 kVp at each stage. The test set of 140 radiographs was digitized, identical bony regions of interest selected from the density-corrected images of each specimen, the regions digitally filtered to reduce film-grain noise, and textural features computed on a line-to-line basis. Correlation analysis identified a set of features whose changes consistently exhibited a moderate-to-strong linear association with bone mineral loss over a wide range of decalcification. Repeated measures analysis of variance was subsequently applied to this set to measure the minimal decalcification that could be detected by each feature under optimal conditions of X-ray beam angulation (0/spl deg/) and suboptimal conditions (/spl plusmn/5/spl deg/). The best performing features were mean intensity, gradient, Laws' texture energy measures, and fractal dimension which detected 5.7% bone decalcification at optimal beam angulation and 9.4-12.6% at suboptimal angulation.