Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

Acharya, U. Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

doi:10.1016/j.compbiomed.2017.09.017

Cited by 1,263 publications

(615 citation statements)

References 45 publications

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“…Mostly implemented pooling functions to reduce features and to resist the slight translation effect are stochastic, average, and maximum pooling functions . To explain these functions shortly, let a pooling region be W .…”

Section: Proposed Methodsmentioning

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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Section: Proposed Methodsmentioning

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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“…In terms of artificial intelligence, it is essential to reveal how artificial neural networks establish complex dependencies in nonlinear and nonstationary signals in order to reach significant progress in the development of ANNbased systems. Along with classification of motor-related EEG, it is also especially important to classify other types of brain activity, such as epilepsy patterns [35], sleep stages [36], and mental disorders [37].…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

et al. 2018

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We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

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“…This approach overcomes the limitations of shallow learning and does not require manual design to extract image features. The accurate diagnosis obtained by using such algorithms can be comparable to the diagnosis of experts . For example, deep learning has been used to determine survival subpopulations of HCC patients based on multi‐omics features .…”

Section: Introductionmentioning

confidence: 99%

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning

Lin

Wei

Wang

et al. 2019

Journal of Biophotonics

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In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label‐free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep‐learning algorithm for classifying HCC differentiation to produce an innovative computer‐aided diagnostic method. Convolutional neural networks based on the VGG‐16 framework were trained using 217 combined two‐photon excitation fluorescence and second‐harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label‐free, automated methods for various tissues, diseases and other related classification problems.

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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

Cited by 1,263 publications

References 45 publications

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

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

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning

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