Yang Wenlu, Pilozzi Alexander, Huang Xudong
Department of Electrical Engineering, Information Engineering College, Shanghai Maritime University, Shanghai 200135, China.
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.
Biomedicines. 2021 Apr 6;9(4):386. doi: 10.3390/biomedicines9040386.
Alzheimer's disease (AD) is by far the most common cause of dementia associated with aging. Early and accurate diagnosis of AD and ability to track progression of the disease is increasingly important as potential disease-modifying therapies move through clinical trials. With the advent of biomedical techniques, such as computerized tomography (CT), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI), large amounts of data from Alzheimer's patients have been acquired and processed from which AD-related information or "signals" can be assessed for AD diagnosis. It remains unknown how best to mine complex information from these brain signals to aid in early diagnosis of AD. An increasingly popular technique for processing brain signals is independent component analysis or blind source separation (ICA/BSS) that separates blindly observed signals into original signals that are as independent as possible. This overview focuses on ICA/BSS-based applications to AD brain signal processing.
阿尔茨海默病（AD）是目前与衰老相关的痴呆症最常见的病因。随着潜在的疾病修饰疗法进入临床试验阶段，AD的早期准确诊断以及追踪疾病进展的能力变得越来越重要。随着生物医学技术的出现，如计算机断层扫描（CT）、脑电图（EEG）、脑磁图（MEG）、正电子发射断层扫描（PET）、磁共振成像（MRI）和功能磁共振成像（fMRI），已经获取并处理了来自阿尔茨海默病患者的大量数据，从中可以评估与AD相关的信息或“信号”以用于AD诊断。目前尚不清楚如何最好地从这些脑信号中挖掘复杂信息以辅助AD的早期诊断。一种越来越流行的处理脑信号的技术是独立成分分析或盲源分离（ICA/BSS），它将盲目观察到的信号分离成尽可能独立的原始信号。本综述重点关注基于ICA/BSS的应用于AD脑信号处理的研究。
