Deep tendon reflexes are one of the main parameters of the neurological examination in many diseases. Reflex responses increase in upper motor neuron diseases due to a lack of suprasegmental control such as spasticity and rigidity. This information provided by the reflex response makes it an indispensable element of neurological examination. However, an important limitation is that this assessment is subjective. In this study, EMG and kinesiology measurements were recorded together during the assessment of the patellar T reflex in healthy control, spasticity, and Parkinson's disease groups. Nine kinesiologic and three electrophysiologic features were extracted. We validated the proposed method with three healthy participants by ten repeated measurements on 6 different days and we observed that angular velocity is the most stable parameter. Clustering of different groups determined with K-clustering and artificial neural network used for classification with kinesiological and EMG inputs. Our findings show that reflex grade can be determined with high accuracy (Acc = 98.6) in a large population for both pathological and healthy groups and angular velocity is sufficient for reflex grading. Therefore, we think that our study will contribute to the literature by providing an approach with high reliability and reproducibility in the quantitative assessment of reflexes.
Keywords: Muscle spasticity; Neural network; Parkinson’s disease; Tendon reflex.
© 2022. International Federation for Medical and Biological Engineering.