杠杆(统计)
主成分分析
方向性
计算机科学
人工智能
维数之咒
降维
机器学习
校长(计算机安全)
模式识别(心理学)
心理学
变化(天文学)
组分(热力学)
数学
作者
Vincenzo Catrambone,Gaetano Valenza
标识
DOI:10.1109/embc58623.2025.11253516
摘要
Brain-Heart Interplay (BHI) research is gaining increasing attention in the scientific community. However, the complexity and time-varying nature of BHI pose significant methodological challenges linked to the numerous variables involved, including directionality (i.e., descending brain-to-heart and ascending heart-to-brain), oscillatory dynamics, and scalp locations. It remains unclear whether the spatio-temporal variability of BHI can be effectively captured by reducing the dimensionality of the problem. In this study, we leverage a principal component analysis (PCA)-based approach to investigate the existence of a synergistic BHI. Experimental results on a publicly available EEG-ECG dataset of healthy subjects in resting state confirm the existence of principal components in BHI dimensions, highlighting distinct characteristics based on directionality and oscillatory frequency.Clinical relevance: The proposed methodology could provide novel biomarkers to support the diagnosis of neurological, psychiatric, and cardiovascular disorders.
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