Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms

Raghu, S.

doi:10.1016/j.eswa.2018.06.031

Cited by 192 publications

(104 citation statements)

References 48 publications

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“…NCA is a supervised learning method for classifying multivariate data into separate classes based on a given distance metric for the data. Functionally, it serves the same purpose as the kNN algorithm and directly employs a related concept called the stochastic proximity neighbor [19]. Neighbor factor analysis aims to learn the distance metric by finding a linear transformation of the input data to maximize the average LOO (leave-one-out) classification performance in the transformed space [30].…”

Section: Neighborhood Component Analysis (Nca)mentioning

confidence: 99%

“…Neighborhood component analysis (NCA), a major dimension reduction method, is applied to test multiple objects or to identify biases of numerous results from different locations. NCA has been found to be effective in reducing dimensions by identifying trends according to which large amounts of data are contained [19]. NCA is a method for finding feature spaces such that the probabilistic nearest neighbor algorithm provides the best accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

et al. 2020

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The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF.

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Section: Neighborhood Component Analysis (Nca)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

et al. 2020

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“…In [13], authors achieved an accuracy of 94.5% using LS-SVM classifier, with the extracted entropy features from the decomposed sub-bands using the orthogonal wavelet filter banks, which are based on the time-frequency domain. In [14], the neighborhood component analysis (NCA) with support vector machine (SVM) attained the sensitivity of 97.6%, the accuracy of 96.1%, and specificity of 94.4% in distinguishing the NFC and FC EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Prasanna

Subathra

Mohammed

et al. 2020

Sensors

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The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

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“…Principal component analysis (PCA) and neighborhood component analysis (NCA) can be used to reduce dimensions while maintaining key features. They have been found to be effective in classifying and reducing dimensions by identifying trends in analyses that contain large amounts of data [21][22][23]. The PCA identifies the attribute combinations that account for the largest difference in data, and the NCA finds feature spaces so that the proximate nearest neighbor algorithm provides the best accuracy.…”

Section: Introductionmentioning

confidence: 99%

Damage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data

et al. 2019

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Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by using general frequency response signals. Therefore, this study used a method that extracted several features that can be derived from the frequency response signal and reduced their dimensions to select features suitable for the evaluation of the soundness of porcelain insulators. The latest machine learning techniques were used to identify correlations and not for basic feature analyses. Two machine learning models were developed using the support vector machine and ensemble methods in MATLAB. Both models showed high reliability in distinguishing between normal and defective porcelain insulators, and they could visualize the distribution area of the data by extracting quantitative values and applying machine learning, rather than simply verifying the frequency response signal.

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Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms

Cited by 192 publications

References 48 publications

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators

Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

Damage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data

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