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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 33; číslo 8; s. 3357 - 3371
Hlavní autoři:	Huang, Zhongwei, Lei, Haijun, Chen, Guoliang, Frangi, Alejandro F., Xu, Yanwu, Elazab, Ahmed, Qin, Jing, Lei, Baiying
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2162-237X 2162-2388 2162-2388
DOI:	10.1109/TNNLS.2021.3052652