Parkinson's Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning From Longitudinal Data

Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised featu...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 33; no. 8; pp. 3357 - 3371
Main Authors: Huang, Zhongwei, Lei, Haijun, Chen, Guoliang, Frangi, Alejandro F., Xu, Yanwu, Elazab, Ahmed, Qin, Jing, Lei, Baiying
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive control
Algorithms
Biomedical imaging
Classification
clinical score prediction
Data structures
Diseases
Embedding
embedding learning
Feature extraction
Iterative methods
longitudinal multimodal data
Lung
Machine learning
Manifolds (mathematics)
Movement disorders
Neurodegenerative diseases
Optimization
Parkinson's disease
Parkinson’s disease (PD)
Regression
Similarity
Sparse matrices
sparse regression
Task analysis
Ultrasonic imaging
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Parkinson's disease (PD) is known as an irreversible neurodegenerative disease that mainly affects the patient's motor system. Early classification and regression of PD are essential to slow down this degenerative process from its onset. In this article, a novel adaptive unsupervised feature selection approach is proposed by exploiting manifold learning from longitudinal multimodal data. Classification and clinical score prediction are performed jointly to facilitate early PD diagnosis. Specifically, the proposed approach performs united embedding and sparse regression, which can determine the similarity matrices and discriminative features adaptively. Meanwhile, we constrain the similarity matrix among subjects and exploit the <inline-formula> <tex-math notation="LaTeX">{l}_{\mathrm {2,p}} </tex-math></inline-formula> norm to conduct sparse adaptive control for obtaining the intrinsic information of the multimodal data structure. An effective iterative optimization algorithm is proposed to solve this problem. We perform abundant experiments on the Parkinson's Progression Markers Initiative (PPMI) data set to verify the validity of the proposed approach. The results show that our approach boosts the performance on the classification and clinical score regression of longitudinal data and surpasses the state-of-the-art approaches.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3052652