RESUMEN

Background: Patients' hand-drawn Archimedes spirals are widely used in the neurological community to grade tremors. These spirals are either drawn on paper and Xeroxed/scanned into digital images or digitizing tablets are used for the drawings. This process introduces artifacts such as variable widths of the drawn lines with varying pixel grey scale values. Xeroxing introduces additional artifacts resulting from paper misalignments. These artifacts and the presence of the reference spiral in the image complicate an automatic extraction of a mathematical spiral signal from the image. New methods: We introduce a mathematical mapping that transforms the image pixels of the patient's hand-drawn spiral into a one-dimensional discrete signal that can be used for mathematical analysis. Results: A cohort of 18 hand-drawn spirals with various artifacts is used to validate our method.We extract the parameters of the discrete signals and show that the signals can be represented by truncating to as few as 150 parameters with a truncation RMS error of 6.26 % across the cohort. Using only 150 features makes machine learning a viable option for future applications. Furthermore, our method can be used to evaluate the frequency and the amplitude of the tremor. Comparison with existing methods: In existing methods, the patient draws the spiral on a digitizing tablet, and features are extracted from this data for machine learning. We recognize that a vast majority of hospitals are still using the pencil-and-paper approach, and there is an abundance of ready-to-be-mined tremor-related data already stored as paper or digitized drawings. Our procedure is equally applicable to Xeroxed documents as well as files generated from digital tablets. Conclusions: We have validated a new procedure requiring minimal user intervention to automatically extract a patient's hand-drawn spiral as a discrete mathematical one-dimensional signal from a scanned image or a file from a digital tablet.