Interpretable machine learning for the diagnosis of hyperkinetic movement disorders

We present a machine learning approach to the challenging differentiation of hyperkinetic movement disorders, based on accelerometric sensor data.We address the diagnosis of essential tremor and cortical myoclonus as a specific example.Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) systems are applied directly to power spectra obtained from eight sensors recording upper body movements.We find excellent validation performance of the classifiers.Moreover, GMLVQ provides insight into the characteristic patterns of the phenotypes and the importance of particular frequency ranges in the spectra.We demonstrate that the explanatory power of the classifier is further enhanced when integrating information from several tasks per subject.