PPGCN: Phase-Aligned Periodic Graph Convolutional Network for Dual-Task-Based Cognitive Impairment Detection

• Published in: IEEE access Vol. 12; pp. 37679 - 37691
• Main Authors: Godo, Akos, Wu, Shuqiong, Okura, Fumio, Makihara, Yasushi, Ikeda, Manabu, Sato, Shunsuke, Suzuki, Maki, Satake, Yuto, Taomoto, Daiki, Yagi, Yasushi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Cognition; Color imagery; Convolutional neural networks; Dementia; Detection algorithms; Feature extraction; Impairment; Motors; Pipelines; Task analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3371517

Early detection methods for cognitive impairment are crucial for its effective treatment. Dual-task-based pipelines that rely on skeleton sequences can detect cognitive impairment reliably. Although such pipelines achieve state-of-the-art results by analyzing skeleton sequences of periodic stepping motion, we propose that their performance can be improved by decomposing the skeleton sequence into representative phase-aligned periods and focusing on them instead of the entire sequence. We present the phase-aligned periodic graph convolutional network, which is capable of processing phase-aligned periodic skeleton sequences. We trained it with a cross-modality feature fusion loss using a representative dataset of 392 samples annotated by medical professionals. As part of a dual-task cognitive impairment detection pipeline that relies on two-dimensional skeleton sequences extracted from RGB images to improve its general usability, our proposed method outperformed existing approaches and achieved a mean sensitivity of 0.9231 and specificity of 0.9398 in a four-fold cross-validation setup.