TY - GEN
T1 - Analysis of Interpretable Handwriting Features to Evaluate Motoric Patterns in Different Neurodegenerative Diseases
AU - Kairamkonda, D. D.
AU - Mandaleeka, P. S.
AU - Favaro, A.
AU - Motley, C.
AU - Butala, A.
AU - Oh, E. S.
AU - Stevens, R. D.
AU - Dehak, N.
AU - Moro-Velazquez, L.
N1 - Funding Information:
This work was funded by the Richman Family Precision Medicine Center of Excellence – Venture Discovery Fund. We want to thank Dr Jiri Mekyska and his laboratory for sharing their invaluable feature extraction library to obtain some of the handwriting features employed in this study.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Clinicians currently use handwriting as one of the methods to establish the presence and monitor the progression of neurodegenerative diseases (NDs). While common handwriting evaluation methods are valuable means to detect fine motor and cognitive impairments associated with NDs, these are observer-dependent and subjective. In the present study, we analyzed a broad array of interpretable features, some proposed for the first time in this study, obtained from online handwriting data of participants with NDs and control subjects (CTRL). ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). Hand-writing data from three different neuropsychological tasks was used: Copy Text task, Copy Cube task, and Copy Image task. Then, we arranged three complementary sets of features and conducted a statistical analysis to test their significance between groups. Overall results suggested that subjects with AD reported a significantly higher (p < 0.05) amount of data points and total duration with respect to the CTRL group in almost all the tasks under assessment. On the other hand, subjects with PD showed a significantly lower (p < 0.05) horizontal width (both on tablet and in the air). Even though the AD and PDM groups showed a significantly lower velocity and acceleration (p < 0.05), their number of inversions in velocity and acceleration was significantly greater (p < 0.05), which indicates disfluency in writing. The features that we have used were found to provide good results in differentiating the studied groups and could be considered as part of diagnostic tools for the assessment and monitoring of NDs in clinical trials.
AB - Clinicians currently use handwriting as one of the methods to establish the presence and monitor the progression of neurodegenerative diseases (NDs). While common handwriting evaluation methods are valuable means to detect fine motor and cognitive impairments associated with NDs, these are observer-dependent and subjective. In the present study, we analyzed a broad array of interpretable features, some proposed for the first time in this study, obtained from online handwriting data of participants with NDs and control subjects (CTRL). ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). Hand-writing data from three different neuropsychological tasks was used: Copy Text task, Copy Cube task, and Copy Image task. Then, we arranged three complementary sets of features and conducted a statistical analysis to test their significance between groups. Overall results suggested that subjects with AD reported a significantly higher (p < 0.05) amount of data points and total duration with respect to the CTRL group in almost all the tasks under assessment. On the other hand, subjects with PD showed a significantly lower (p < 0.05) horizontal width (both on tablet and in the air). Even though the AD and PDM groups showed a significantly lower velocity and acceleration (p < 0.05), their number of inversions in velocity and acceleration was significantly greater (p < 0.05), which indicates disfluency in writing. The features that we have used were found to provide good results in differentiating the studied groups and could be considered as part of diagnostic tools for the assessment and monitoring of NDs in clinical trials.
KW - copy tasks
KW - handwriting
KW - interpretability
KW - kinematics
KW - micrographia
KW - neurodegenerative diseases
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U2 - 10.1109/SPMB55497.2022.10014966
DO - 10.1109/SPMB55497.2022.10014966
M3 - Conference contribution
AN - SCOPUS:85147673021
T3 - 2022 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2022 - Proceedings
BT - 2022 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2022
Y2 - 3 December 2022
ER -