Motor Signal Intelligent Processing in Huntington Disease Diagnosis

Movement disorder is one of the common symptoms of Huntington’s disease (HD) that afflicts patients in controlling their movements. The main objective of this paper is to detect abnormal patterns of the foot during gating. The total number of 40 subjects included 16 healthy and 20 HD patients were investigated. All of the subjects were asked to gait in a 70m straight route. The time and time-frequency domain analyses have been used. The support vector machine (SVM) was performed to classify the normal and HD groups. The results showed that using a radial basis function with a combination of time and time-frequency features could better detect the abnormal patterns generated by the motor signal. The classification results for differentiating normal and HD subjects were achieved to the sensitivity and specificity of 93.46% and 91.93%, respectively. This study showed that the proposed algorithm is useful for the early diagnosis of gait pathologies. The results showed accurate performance of this method with the potentials to replace foot sensors signals as a means of classifying gait patterns.

Keywords:

Motor signals , Huntington’s disease , Feature selection , Classification