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Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\text {NeurDNet}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mtext>NeurDNet<\/mml:mtext>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\text {NeurDNet}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mtext>NeurDNet<\/mml:mtext>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\text {NeurDNet}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mtext>NeurDNet<\/mml:mtext>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of <jats:inline-formula><jats:alternatives><jats:tex-math>$$95.55\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>95.55<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.<\/jats:p>","DOI":"10.1038\/s41598-021-88919-9","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T10:07:04Z","timestamp":1620209224000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A deep explainable artificial intelligent framework for neurological disorders discrimination"],"prefix":"10.1038","volume":"11","author":[{"given":"Soroosh","family":"Shahtalebi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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