scholarly journals Fast Enhanced Exemplar-Based Clustering for Incomplete EEG Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Anqi Bi ◽  
Wenhao Ying ◽  
Lu Zhao
Keyword(s):  
Machine Learning ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Target Function ◽  
The Other ◽  
Eeg Signal ◽  
Similarity Matrix ◽  
Pairwise Similarity ◽  
Eeg Signals ◽  
Brain Science

The diagnosis and treatment of epilepsy is a significant direction for both machine learning and brain science. This paper newly proposes a fast enhanced exemplar-based clustering (FEEC) method for incomplete EEG signal. The algorithm first compresses the potential exemplar list and reduces the pairwise similarity matrix. By processing the most complete data in the first stage, FEEC then extends the few incomplete data into the exemplar list. A new compressed similarity matrix will be constructed and the scale of this matrix is greatly reduced. Finally, FEEC optimizes the new target function by the enhanced α-expansion move method. On the other hand, due to the pairwise relationship, FEEC also improves the generalization of this algorithm. In contrast to other exemplar-based models, the performance of the proposed clustering algorithm is comprehensively verified by the experiments on two datasets.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

Research on multi-view clustering algorithm on epileptic EEG signal

2022 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Signal Identification ◽  
Advanced Technologies ◽  
New Development ◽  
New Ideas ◽  
Development Paths ◽  
Eeg Signal Processing

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

Research on multi-view clustering algorithm on epileptic EEG signal

2022 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Signal Identification ◽  
Advanced Technologies ◽  
New Development ◽  
New Ideas ◽  
Development Paths ◽  
Eeg Signal Processing

Epilepsy is caused by the abnormal discharge of the patient's brain. Smart medical uses advanced technologies such as signal recognition and machine learning to identify and analyze the biological signals fed back from the subjects’ brain electrical signals and provide diagnostic results. In the past, doctors used their own experience and theoretical knowledge to judge whether there are characteristic signals by observing the subject’s EEG signal to realize the judgment of the condition. This method of diagnosis through observation often infuses the doctor's own subjective judgment, leading to misdiagnosis of the condition and low diagnosis and treatment efficiency. With the continuous development of advanced technologies such as artificial intelligence and signal recognition, this provides new ideas for the realization of EEG signal recognition and processing technology and opens up new development paths. This article is based on epilepsy EEG signal data, realizes EEG signal processing and uses machine learning methods to realize EEG signal identification and diagnosis.

scholarly journals A Novel Transfer Enhanced α -Expansion Move Learning Model for EEG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jiangwei Cai ◽  
Lu Zhao ◽  
Anqi Bi
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Bayesian Probability ◽  
Utilization Rate ◽  
Similarity Matrix ◽  
Learning Models ◽  
Eeg Signals ◽  
Clustering Model ◽  
Target Data ◽  
Machine Learning Models

In this paper, we focus on recognizing epileptic seizure from scant EEG signals and propose a novel transfer enhanced α -expansion move (TrEEM) learning model. This framework implants transfer learning into the exemplar-based clustering model to improve the utilization rate of EEG signals. Starting from Bayesian probability theory, by leveraging Kullback-Leibler distance, we measure the similarity relationship between source and target data. Furthermore, we embed this relationship into the calculation of similarity matrix involved in the exemplar-based clustering model. Then we sum up a new objective function and study this new TrEEM scheme earnestly. We optimize the proposed TrEEM model by borrowing the mechanism utilized in EEM. In contrast to other machine learning models, experiments based on synthetic and real-world EEG datasets show that the performance of the proposed TrEEM is very promising.

scholarly journals INCREASING QUALITY OF CLASSIFYING OBJECTS USING NEW METRICS OF CLUSTERING

2019 ◽  
pp. 106-114
Author(s):  
Iscandar Maratovich Azhmukhamedov ◽  
Raisa Yurevna Demina
Keyword(s):  
Machine Learning ◽  
Rand Index ◽  
The Other ◽  
Similarity Matrix ◽  
Clustering Method ◽  
Subject Areas ◽  
Learning Set ◽  
Original Picture ◽  
Object Similarity

The article touches upon one of the main problems of machine learning - clustering objects. It has been widely used in various subject areas: marketing, sociology, psychology, etc. Clusterization algorithms, as a rule, are based on a metric that reflects the distance between objects. However, in some cases it is not practical to use the distance between objects. In certain situations, it is possible to say that one object is similar to the other, the latter being not similar to the former. The original picture and its copy may serve as an example. For such cases, a measure of object similarity is proposed in the work, which shows how many features of one object are contained in another one. A similarity matrix is built on this measure, the analysis of which allows revealing clusters of mutually similar objects. When testing the proposed clustering method, the Rand index (the proportion of correctly connected or unrelated objects) made 0.93. There has been proposed an algorithm that allows to form a set of objects absolutely different from each other. A set of objects formed in this way can later become a learning set for classifiers and increase their fidelity in recognition.

scholarly journals Epileptic EEG signal classifications based on DT-CWT and SVM classifier

2021 ◽  
Author(s):  
Deivasigamani S ◽  
◽  
Senthilpari C ◽  
Wong Hin Yong ◽  
Rajesh P.K. ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
The Other ◽  
Support Vector ◽  
Svm Classifier ◽  
Medical Procedure ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Sensitivity Specificity ◽  
The Mind

Contamination in human cerebrum causes the mind issue which is as Epilepsy. The contaminated territory in the cerebrum area creates the unpredictable example signals as focal signs and the other sound locales in the mind produce the standard example signals as non-focal sign. Henceforth, the discovery of focal signs from the non-focal signs is a significant for epileptic medical procedure in epilepsy patients. This paper proposes a straightforward and proficient technique for EEG (Electroencephalogram) signals orders utilizing SVM (Support Vector Machine) classifier. The exhibition of the proposed EEG signals characterization framework is assessed as far as Sensitivity, Specificity, and Accuracy.

scholarly journals Recognition of Imbalanced Epileptic EEG Signals by a Graph-Based Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Zhou ◽  
Xiongtao Zhang ◽  
Zhibin Jiang
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Learning Methods ◽  
Imbalanced Classification ◽  
Machine Learning Methods ◽  
Learning Machine

Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph-based extreme learning machine method (G-ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

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