Yan Niu

TL;DR: A convolutional neural network based on raw EEG signals instead of manual feature extraction was used and the effective identification of the three cases using time domain signals as input samples is achieved for only some patients, but the classification accuracies of frequency domain signals are significantly increased compared to timedomain signals.

TL;DR: The novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in mild cognitive impairment (MCI) and AD patients revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls and indicated that declines in PE might be related to changes in regional functional homogeneity in AD.

TL;DR: The results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human.

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: The complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales and may provide an imaging biomarker of cognitive impairments in MCI and AD.