Abstract
Mobile edge computing (MEC) has the ability of pattern recognition and intelligent processing of real‐time data. Electroencephalogram (EEG) is a very important tool in the study of epilepsy. It provides rich information that can not be provided by other physiological methods. In the automatic classification of EEG signals by intelligent algorithms, feature extraction and the establishment of classifiers are both very important steps. Different feature extraction methods, such as time domain, frequency domain, and nonlinear dynamic feature methods, contain independent and diverse specific information. Using multiple forms of features at the same time can improve the accuracy of epilepsy recognition. In this paper, we apply metric learning to epileptic EEG signal recognition. Inspired by the equidistance constrained metric learning algorithm, we propose multifeature metric learning based on enhanced equidistance embedding (MMLE3) for EEG recognition of epilepsy. The MMLE3 algorithm makes use of various forms of EEG features, and the feature weights are adaptively weighted. It is a big advantage that the feature weight vector can be adjusted adaptively, without manual adjustment. The MMLE3 algorithm maximizes the distance between the samples constrained by the cannot‐link, and the samples of different classes are transformed into equidistant; meanwhile, MMLE3 minimizes the distance between the data constrained by the must‐link, and the samples of the same class are compressed to one point. Under the premise that the various feature classification tasks are consistent, MMLE3 can fully extract the associated and complementary information hidden between the features. The experimental results on the CHB‐MIT dataset verify that the MMLE3 algorithm has good generalization performance.
Highlights
Mobile edge computing (MEC) converges cloud computing capabilities and Internet service environment to the edge of the network, which can provide services to users nearby, and effectively makes up for the deficiencies of cloud computing [1]
MMLE3 98.09 97.60 95.59 98.14 97.29 96.59 98.35 97.71 96.73 96.97 97.33 96.85 97.40 97.78 97.75 98.25 97.75 97.83 97.23 97.62 97.31 97.44 global metric learning algorithm, which made the separation of different categories of samples greater in EEG signal recognition
This study explores how to improve the classification accuracy of epileptic EEG based on various feature extraction methods and metric learning algorithm
Summary
Mobile edge computing (MEC) converges cloud computing capabilities and Internet service environment to the edge of the network, which can provide services to users nearby, and effectively makes up for the deficiencies of cloud computing [1]. Kaya et al [5] used the histogram features based on local binary patterns (LBP) together with Bayesian networks to classify epileptic seizures Another widely used strategy is to extract frequency domain features from a given EEG signal. Chandel et al [7] proposed a combination of features based on ternary wavelet decomposition to predict the onset and termination of epilepsy This method extracted standard deviation, variance, and high-order moments to represent the characteristics of different EEG activities and used linear discriminant analysis and K-nearest neighbor (KNN) classifiers to classify EEG between seizure and interictal periods. Xiang et al [8] developed a feature extraction algorithm using fuzzy entropy This method first calculated the fuzzy entropy of EEG signals from different epileptic states, performed feature selection, and used a support vector machine for prediction. Appropriate filtering and independent quantity analysis were carried out to remove noise and artifacts, and the proportional operation was carried out to obtain the
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