作者
Saman Fouladi, Ali A Safaei, Nadia Mammone, Foad Ghaderi, Mohammad Javad Ebadi
发表日期
2022/7
期刊
Cognitive Computation
卷号
14
期号
4
页码范围
1247-1268
出版商
Springer US
简介
The early diagnosis of subjects with mild cognitive impairment (MCI) is an effective appliance of prognosis of Alzheimer’s disease (AD). Electroencephalogram (EEG) has many advantages compared to other methods in the analysis of AD in an early stage. In this paper, two different deep learning (DL) architectures, including modified convolutional (CNN) and convolutional autoencoder (Conv-AE) neural networks (NNs), are proposed for classifying subjects into AD, mild cognitive impairment (MCI), and healthy control (HC) data based on scalp EEG recordings. The database includes 19-channel EEG recorded from 61 healthy control, 56 MCI, and 63 AD subjects. Time–frequency representation (TFR) is used to extract desirable features from EEG signals. Continuous wavelet transform (CWT) with Mexican hat function (MHf) as its mother wavelet is used for the selected TFR. The average accuracy obtained for the …
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S Fouladi, AA Safaei, N Mammone, F Ghaderi… - Cognitive Computation, 2022