Comparison of computer-key-hold-time and alternating-finger-tapping tests for early-stage Parkinson’s disease

• Published in: PloS one Vol. 14; no. 6; p. e0219114
• Main Authors: Lan, Boon Leong, Yeo, Jacob Hsiao Wen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.06.2019; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Biology and Life Sciences; Biomarkers; Body fat; Cardiac arrhythmia; Care and treatment; Case-Control Studies; Computer and Information Sciences; Computers; Engineering schools; Fingers - physiopathology; Humans; Keyboards; Machine learning; Medical research; Medicine and Health Sciences; Motor Skills - physiology; Motor task performance; Movement - physiology; Novels; Parkinson disease; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Physical Sciences; Research and Analysis Methods; Skewness; Standard deviation; Task Performance and Analysis; Time Factors; Variation; Malaysia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0219114

Giancardo et al. recently introduced the neuroQWERTY index (nQi), which is a novel motor index derived from computer-key-hold-time data using an ensemble regression algorithm, to detect early-stage Parkinson's disease. Here, we derive a much simpler motor index from their hold-time data, which is the standard deviation (SD) of the hold-time fluctuations, where fluctuation is defined as the difference between successive natural-log of hold time. Our results show the performance of the SD and nQi tests in discriminating early-stage subjects from controls do not differ, although the SD index is much simpler. There is also no difference in performance between the SD and alternating-finger-tapping tests.