Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC

Ke, Hengjin; Chen, Dan; Li, Xiaoli; Tang, Yunbo; Shah, Tejal; Ranjan, Rajiv

doi:10.1109/access.2018.2810882

Cited by 85 publications

(41 citation statements)

References 30 publications

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“…Weight initialization is performed conforming to that proposed in the work of He et al and batch normalization applies to the network . Two strategies are adopted to reduce overfitting of the classifier, that is, early stopping and dropout with the same strategy of our previous work . The objective is to minimize the mean squared error in the CNN.…”

Section: System Architecture and Implementationmentioning

confidence: 99%

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Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Dan

Shah

et al. 2018

Softw Pract Exp

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Summary Brain healthcare, when supported by Internet of Things, can perform online and accurate analysis of brain big data for the classification of multivariate Electroencephalogram (EEG), which is a prerequisite for the recent boom in neurofeedback applications and clinical practices. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic nonstationarity determined by the evolution of brain states; and (2) the lack of a user‐friendly computing platform to sustain the complicated analytics. This study presents the design of an online EEG classification system aided by Cloud centering on a lightweight Convolutional Neural Network (CNN). The system incrementally trains the CNN on Cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a High Convolutional Layer to gain the ability of processing high‐dimensional EEG segments. The number of hidden layers is minimized to ensure the efficiency of training. The lightweight CNN adopts an “hourglass” block of fully connected layers to reduce the number of neurons quickly toward the output end. A case study of depression evaluation has been performed against raw EEG datasets to distinguish between (1) Healthy and Major Depression Disorder with an accuracy, sensitivity, and specificity of [98.59% ± 0.28%], [97.77% ± 0.63%], and [99.51% ± 0.19%], respectively; and (2) Effective and Noneffective treatment outcome with an accuracy, sensitivity, and specificity of [99.53% ± 0.002%], [99.50% ± 0.01%], and [99.58% ± 0.02%], respectively. The results show that the classification can be completed several magnitudes faster when EEG is collected on the gateway (several milliseconds vs. 4 seconds).

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Section: System Architecture and Implementationmentioning

confidence: 99%

“…38 Two strategies are adopted to reduce overfitting of the classifier, that is, early stopping and dropout with the same strategy of our previous work. 23 The objective is to minimize the mean squared error in the CNN. The CNN processes the raw EEG data as the initial inputs in model training.…”

Section: Model Training and Testingmentioning

confidence: 99%

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Dan

Shah

et al. 2018

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

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“…Meanwhile, epilepsy diagnosis has been also attracted much attention in the area of pattern recognition [15]- [17]. It normally relies on feature extraction and pattern classification techniques.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis

Chen

Xie

et al. 2020

IEEE Access

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Electroencephalogram (EEG) contains important physiological information that can reflect the activity of human brain, making it useful for epileptic seizure detection and epilepsy diagnosis. However visual inspection of large amounts of EEG by human expert is time-consuming, and meanwhile there are often inconsistences in judgement between physicians. In this paper, we develop a unified framework for early epileptic seizure detection and epilepsy diagnosis, which includes two phases. In the first phase, the signal intensity is first calculated for each data point of the given EEG, enabling the well-known autoregressive moving average (ARMA) model to characterize the dynamic behavior of the EEG time series. The residual error between the predicted value of learned ARMA model and the actually observed value is used as the anomaly score to support a null hypothesis testing for making epileptic seizure decision. The epileptic seizure detection phase can provide a quick detection for anomaly EEG patterns, but the resulting suspicious segment may include epilepsy or other disordering EEG activities thus required to be identified. Therefore, in the second phase, we use pattern recognition technique to classify the suspicious EEG segments. In particular, we propose a new and practical classifier based on a pairwise of one-class SVMs for epilepsy diagnosis. The proposed classifier requires normal and epilepsy data for training, but can recognize normal, epilepsy and even other disorders that would not be trained in the training samples. This point is practical and meaningful in real clinic scenarios as the input EEG may include other brain disordering diseases besides of epilepsy. We conducted experiments on the publicly-available Bern-Barcelona and CHB-MIT EEG database, respectively, to validate the effectiveness of the proposed framework, and our method achieved classification accuracy of 93% and 94% on them. Comprehensive experimental results, outperforming the state-of-the-arts, suggest its great potentials in real applications.

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“…Feature points are detected in an image, and the region around the feature point is given as input to a CNN to obtain the feature vector. In large-scale visual recognition experimentation, CNNs have shown exceptional performance with VGGNet [20] and GoogLeNet [21]. These networks have been proven to be very discriminative feature descriptors.…”

Section: Introductionmentioning

confidence: 99%

Feature Point Extraction and Tracking Based on a Local Adaptive Threshold

Yang

Chen

2020

IEEE Access

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Navigation, environment perception and localization are important capabilities of intelligent vehicles. In this paper, environmental perception and localization from binocular vision are studied. First, an outdoor feature point extraction algorithm that uses a local adaptive threshold is proposed to acquire environmental information. The algorithm filters feature points by setting adaptive parameters and calculating each pixel threshold with a dynamic local threshold. Second, an accurate method for feature point tracking is proposed for localization. We present exhaustive evaluation in 4 major scenarios from the most popular datasets. Evaluating the proposed method with traditional and state-of-the-art extraction methods and experimental results demonstrates that when the brightness decreases or increases, the performance of the proposed method is stable in terms of the number of feature points, the calculation speed and the overall repetition rate. Our proposed tracking method outperforms state-of-the-art tracking methods in terms of the root mean square error (RMSE) and the errors in the dimensions in the scenarios. INDEX TERMS Feature extraction, tracking, oriented FAST and rotated BRIEF(ORB), features from accelerated segment test (FAST), root mean squared error (RMSE).

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Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC

Cited by 85 publications

References 30 publications

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN

A Unified Framework and Method for EEG-Based Early Epileptic Seizure Detection and Epilepsy Diagnosis

Feature Point Extraction and Tracking Based on a Local Adaptive Threshold

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