Spectral entropy provides separation between Alzheimer’s disease patients and controls: a study of fNIRS

Abstract

Functional near-infrared spectroscopy (fNIRS) is commonly used as a non-invasive tool to measure cerebral neurovascular dynamics. Its potential for diagnostics of various brain disorders has been already demonstrated in many recent studies, including Alzheimer’s disease (AD). fNIRS studies are usually based on comparing hemoglobin measurements at baseline and during a specific task. At present, many proposed methods using fNIRS to diagnose AD involve certain tasks, which may be challenging for the elderly and patients with cognitive decline. Here, we propose a method to characterize AD patients and control in resting state, by applying spectral entropy (SE) analysis on oxyhemoglobin and deoxyhemoglobin, HbO and HbR, respectively, and total hemoglobin (HbT) based on fNIRS signals measured from the left and right sides of the forehead. We applied SE to very low frequency (VLF) (0.008–0.1 Hz), respiratory (0.1–0.6 Hz), and cardiac (0.6–5 Hz) bands to find out which band delivered the optimum result. Next, a t test with 0.05 significant level was performed to compare SE values of AD patients and controls. Results from the VLF band looked promising as SE values from AD patients were always significantly higher than those from controls. In addition, this phenomenon was consistent for both sides of the forehead. However, significant differences in SE values in the respiratory band were found from the left hemisphere only, and in the cardiac band from the right hemisphere only. SE value from the VLF band supports a strong argument that it provides good predictability related to the development of AD. We demonstrated that SE of brain fNIRS signal can be an useful biomarker for Alzheimer’s disease pathology.

Editorial on the special issue on brain physiology meets complex systems

“…Ferdinando et al. [ 23 ] proposed a method for separating patients with Alzheimer’s disease from a control group at rest. They showed that the spectral entropy of brain signals recorded with functional near-infrared spectroscopy is a useful biomarker for Alzheimer’s disease.…”

Functional near-infrared spectroscopy for human brain age group classification by machine learning

“…In addition to noise attenuation, the bandwidth specific features are assumed to contain information related to different physiological events and could contain more predictive power in the brain aging assessment. Spectral entropy, fractional amplitude of physiological fluctuations (fAPF) and relative power derived features have been used in fNIRS and EEG studies related to aging and AD research [18,[27][28][29]. Furthermore, pulse shape derived from fNIRS has been used to distinguish AD subjects from healthy controls [11].…”

“…The results have shown age related differences in the PFC activation [13][14][15][16]. Furthermore, fNIRS has been used to characterize differences between younger and older adults, and mild cognitive-impairment (MCI) and AD groups [17,18], and to show age-related differences in within-session trainability of hemodynamic response. [19] The results of the previous findings suggest potential for the use of fNIRS in the study of aging.…”

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

“…It calculates the average rate at which a stochastic data source produces information. It also measures the spread of the data and is most often used for the assessment of the dynamic order of a system [94]. Shannon's Entropy is represented as…”