A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings

Ieracitano, Cosimo; Mammone, Nadia; Bramanti, Alessia; Hussain, Amir; Morabito, Francesco Carlo

doi:10.1016/j.neucom.2018.09.071

Cited by 194 publications

(118 citation statements)

References 23 publications

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“…A CNN model can accommodate geometric deformation, and the receptive field/convolutional kernel can be readily understood, and the forms of high-level features to identify are detected [ 39 ]. Therefore, numerous studies [ 40 , 41 , 42 ] have employed CNNs as classifiers to identify EEG signals. Network topology is the crucial feature in a CNN algorithm.…”

Section: Methodsmentioning

confidence: 99%

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

Yang

Nguyen

2020

Sensors

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The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.

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Section: Methodsmentioning

confidence: 99%

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

Yang

Nguyen

2020

Sensors

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“…In each submap, the mean (m), standard deviation (d), skewness (v), and kurtosis (k) were estimated. The selected features have been successfully validated in [16][17][18]. The employed features have been extensively described in previous sections.…”

Section: Eeg Feature Extractionmentioning

confidence: 99%

“…In [15], Morabito et al extracted different statistical features, such as mean (µ), standard deviation (σ), and skewness (v), from nontraditional sub-bands in the time-frequency maps of EEG signals. In many studies [15][16][17][18], the statistical features µ, σ, and v provided very robust classification scores. Using artificial intelligence (AI) algorithms, Gasparini et al [15] were able to discriminate EEG time series of PNES from healthy controls.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

Varone

Gasparini

Ferlazzo

et al. 2020

Sensors

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The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1–32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

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“…They provided training of the three‐layered network with mini batch gradient descent and used some convolution layers for pretraining with an AE rather than final training. Also, different approaches based on ElectroEncephaloGraphy (EEG) images have been proposed . However, deep learning‐based network models have many algorithmic components to be designed, such as architecture design (number of layers, filter width and sizes, and regularization layers), activation functions, and optimization functions, which considerably affect the performance of the method.…”

Section: Introductionmentioning

confidence: 99%

“…Also, different approaches based on ElectroEncephaloGraphy (EEG) images have been proposed. [21][22][23] However, deep learning-based network models have many algorithmic components to be designed, such as architecture design (number of layers, filter width and sizes, and regularization layers), activation functions, and optimization functions, which considerably affect the performance of the method. For instance, learning with Rectified Linear Unit (ReLU) can reduce performance in gradient descent operations because all gradient values will be zero when the activation values are zero.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network

Göçeri

2019

Numer Methods Biomed Eng

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Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments have the most impact at the early stage of the disease. Neuroimages obtained by different imaging techniques (for example, diffusion tensor‐based and magnetic resonance‐based imaging) provide powerful information and help to diagnose the disease. In this work, a deeply supervised and robust method has been developed using three dimensional features to provide objective and accurate diagnosis from magnetic resonance images. The main contributions are (a) a new three dimensional convolutional neural network topology; (b) a new Sobolev gradient‐based optimization with weight values for each decision parameters; (c) application of the proposed topology and optimizer to diagnose Alzheimer's disease; (d) comparisons of the results obtained from the recent techniques that have been implemented for Alzheimer's disease diagnosis. Experimental results and quantitative evaluations indicated that the proposed network model is able to achieve to extract desired features from images and provides automated diagnosis with 98.06% accuracy.

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A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings

Cited by 194 publications

References 23 publications

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network

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