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  1. Deep convolutional neural network for classification of sleep stages from single-channel EEG signals
    Mousavi Z, Yousefi Rezaii T, Sheykhivand S, Farzamnia A, Razavi SN
    J Neurosci Methods, 2019 08 01;324:108312.
    PMID: 31201824 DOI: 10.1016/j.jneumeth.2019.108312
    Using a smart method for automatic diagnosis in medical applications, such as sleep stage classification is considered as one of the important challenges of the last few years which can replace the time-consuming process of visual inspection done by specialists. One of the problems regarding the automatic diagnosis of sleep patterns is extraction and selection of discriminative features generally demanding high computational burden. This paper provides a new single-channel approach to automatic classification of sleep stages from EEG signal. The main idea is to directly apply the raw EEG signal to deep convolutional neural network, without involving feature extraction/selection, which is a challenging process in the previous literature. The proposed network architecture includes 9 convolutional layers followed by 2 fully connected layers. In order to make the samples of different classes balanced, we used a preprocessing method called data augmentation. The simulation results of the proposed method for classification of 2 to 6 classes of sleep stages show the accuracy of 98.10%, 96.86%, 93.11%, 92.95%, 93.55% and Cohen's Kappa coefficient of 0.98%, 0.94%, 0.90%, 0.86% and 0.89%, respectively. Furthermore, comparing the obtained results with the state-of-the-art methods reveals the performance improvement of the proposed sleep stage classification in terms of accuracy and Cohen's Kappa coefficient.
  2. Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders
    Akramifard H, Balafar MA, Razavi SN, Ramli AR
    J Med Signals Sens, 2021 05 24;11(2):120-130.
    PMID: 34268100 DOI: 10.4103/jmss.JMSS_11_20
    Background: A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

    Method: The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

    Results: The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

    Conclusion: Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

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