Use of Complex Networks for the Automatic Detection and the Diagnosis of Alzheimer’s Disease

Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.

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Use of Complex Networks for the Automatic Detection and the Diagnosis of Alzheimer’s Disease

Cited by 4 publications

References 21 publications

Complex Networks to Differentiate Elderly and Young People

Complex Networks to Differentiate Elderly and Young People

Feature and Time Series Extraction in Artificial Neural Networks for Arousal Detection from Electrodermal Activity

Computational methods of EEG signals analysis for Alzheimer’s disease classification

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