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Title: Essential Tremor Severity Classification Using a Multi-Layer Perceptron and the TETRAS Scale
Essential tremor (ET) is the most prevalent type of movement disorder responsible for inducing tremor in an individual’s limbs. Various scales, such as the Fahn-Tolosa-Marin (FTM) tremor rating scale and The Essential Tremor Rating Assessment Scale (TETRAS), have been developed and used by physicians to classify the severity of ET. While the FTM scale is highly utilized in ET severity diagnosis, it relies on subjective assessments of the tremor. TETRAS, on the other hand, provides a more quantitative analysis of ET severity by ranking the severity of the tremor based on tremor magnitude. However, TETRAS requires a trained professional (such as a neurologist) to be present, and even in such cases, physicians use TETRAS as a metric baseline to visually approximate the severity of the tremor. In this pilot study, a deep neural network (DNN)-based scale is developed to accurately classify ET severity without the presence of trained experts. To validate the developed DNN-based ET classification scale, a preliminary experiment is performed on a single healthy participant during a leg extension exercise. Tremor was artificially induced at the knee using a motorized lower-limb exoskeleton. To enable near real-time ET classification and to enable rapid DNN response, the DNN assessed the severity of ET every 0.5 seconds; utilizing the previous 0.5 seconds of knee-angle data for DNN training and ET severity classification. The results of the preliminary experiment showed that the DNN achieved a training accuracy of 94.80% and a validation accuracy of 95.18%. Additionally, the DNN achieved a training accuracy of 93.63% and a validation accuracy of 94.05% using computer generated knee-angle measurements.  more » « less
Award ID(s):
2230971
PAR ID:
10651805
Author(s) / Creator(s):
 ;  ;  ;  ;  
Publisher / Repository:
IEEE
Date Published:
Page Range / eLocation ID:
1649 to 1654
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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