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

• 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
• DOI: 10.1109/TNNLS.2021.3052652

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.