Abstract
Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation (ALFF) is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform (FSST), which is often used in the field of signal processing to extract the ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform (FFT) through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT.
Highlights
In recent years, with the rapid development of big data, especially artificial intelligence technology, intelligent methods have become increasingly popular in the operation and management optimization of complex large-scale systems, which often appear in ecological environments, communication networks, informatics, biology, and other disciplines
From the point of view of images, Fourier-based synchrosqueezing transform (FSST) detects the results that cannot be resolved by fast Fourier transform (FFT) and their mean coefficients, which proves that FSST is more accurate and sensitive than FFT in data analysis and image extraction and highlighting
Because of the complexity and variety of EEG signals, extracting the information of the signals in time series can effectively reduce the signal ignorance caused by frequency band concentration
Summary
With the rapid development of big data, especially artificial intelligence technology, intelligent methods have become increasingly popular in the operation and management optimization of complex large-scale systems, which often appear in ecological environments, communication networks, informatics, biology, and other disciplines. Extensive research has focused on medicine and health such as deep neural network, neuroscience, and brain science related purposes, data collection, accurate analysis, monitoring, and connecting available medical resources and healthcare services [1,2,3,4,5,6]. E components of a neuron can be categorized as cell body, dendrite, and axon. Once the signal entering the cell body surpasses the sustaining threshold, the neuron is burned, and the signal is transmitted to other neurons through axons [7]. It is considered able to directly reflect the spontaneous synchronous changes of neural activity in a resting state [12, 13] and to some extent reflect the interaction and neural network connection among the relevant brain regions, which has been verified in the research of visual stimulus differences caused by eye opening and closing. ALFF can serve as a starting point for understanding brain diseases and can help reveal many pathological mechanisms, such as some mental diseases [14, 15]. erefore, improving the analytical accuracy of Complexity
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