Jie Sun

TL;DR: The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD.

TL;DR: In this paper, a hybrid deep neural networks (DNNs) was proposed to classify schizophrenia patients and healthy controls using EEG signals, which achieved an average accuracy of 99.22% with fuzzy entropy and 96.34% with fast Fourier transform (FFT).

TL;DR: Compared to other state of art entropy-based feature extraction methods, PFEN showed its potential to be a promising non-linear feature for achieving high accuracy and efficiency in seizure detection and its feasibility towards the development of a real-time EEG-based brain monitoring system for epileptic seizure detection.

TL;DR: Investigation of the distribution and test-retest reliability of four entropy measures and a new entropy algorithm proposed, permutation fuzzy entropy (PFE), in three independent data sets at three levels showed that analyzing fMRI signals with entropy showed strong tissue sensitivity and suggested that PFE and PE had better reliability.

TL;DR: In this paper, the authors performed multiscale sample entropy (MSE) analysis across five time scales to assess differences in resting-state fMRI signal complexity in Schizophrenia, bipolar disorder, and normal controls.