scholarly journals On the Classification of ECG and EEG Signals with Various Degrees of Dimensionality Reduction

Biosensors ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 161
Author(s):  
Monica Fira ◽  
Hariton-Nicolae Costin ◽  
Liviu Goraș
Keyword(s):  
Dimensionality Reduction ◽  
Input Data ◽  
Random Projections ◽  
Signal Acquisition ◽  
Classification Methods ◽  
Eeg Signals ◽  
Laplacian Eigenmaps ◽  
The Third ◽  
Locality Preserving

Classification performances for some classes of electrocardiographic (ECG) and electroencephalographic (EEG) signals processed to dimensionality reduction with different degrees are investigated. Results got with various classification methods are given and discussed. So far we investigated three techniques for reducing dimensionality: Laplacian eigenmaps (LE), locality preserving projections (LPP) and compressed sensing (CS). The first two methods are related to manifold learning while the third addresses signal acquisition and reconstruction from random projections under the supposition of signal sparsity. Our aim is to evaluate the benefits and drawbacks of various methods and to find to what extent they can be considered remarkable. The assessment of the effect of dimensionality decrease was made by considering the classification rates for the processed biosignals in the new spaces. Besides, the classification accuracies of the initial input data were evaluated with respect to the corresponding accuracies in the new spaces using different classifiers.

scholarly journals A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mahshad Ouchani ◽  
Shahriar Gharibzadeh ◽  
Mahdieh Jamshidi ◽  
Morteza Amini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complexity Analysis ◽  
Future Research ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Classification Methods ◽  
Clinical Markers ◽  
Eeg Signals ◽  
Key Steps

This study will concentrate on recent research on EEG signals for Alzheimer’s diagnosis, identifying and comparing key steps of EEG-based Alzheimer’s disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article’s purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer’s disease, extreme Alzheimer’s disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer’s disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer’s disease science.

scholarly journals Algorithms for Classification of Signals Derived From Human Brain

2021 ◽  
Vol 15 ◽  
pp. 1521-1526
Author(s):  
Georgi P. Dimitrov ◽  
Galina Panayotova ◽  
Boyan Jekov ◽  
Pavel Petrov ◽  
Iva Kostadinova ◽  
...  
Keyword(s):  
Random Forest ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Signal Acquisition ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Non Invasive ◽  
Basic Parameters ◽  
Linear Svm

Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.

scholarly journals The Classification of GIS Objects

2021 ◽  
Vol 2096 (1) ◽  
pp. 012002
Author(s):  
Yu N Mironova
Keyword(s):  
Data Processing ◽  
Dimensionality Reduction ◽  
Preliminary Data ◽  
Feature Weighting ◽  
Classification Methods ◽  
Centroid Method ◽  
Spatial Objects ◽  
Spatial Features ◽  
Wide Range

Abstract This paper discusses the current issues of the application of classification and data processing in geoinformation systems. The problems of classification of various objects have been studied in the works of many authors. These include a fairly wide range of problems: decryption of satellite images, pattern recognition, mathematical modeling, etc. In this paper, we study the methods and techniques for classifying objects listed in the literature, as well as preliminary data processing: feature normalization, feature weighting, aggregation, dimensionality reduction, etc. The result of finding spatial features in an attribute space is often a representation of spatial features in the form of an object-feature matrix that reflects the measurement of M features on N spatial features and contains N rows and M columns. To classify spatial objects, you must have a geographical map of these objects and an object-attribute matrix, the rows of which correspond to the spatial objects. In order to properly classify, you need to perform pre-processing of the data, including normalization, weighting, dimensionality reduction, aggregation, and identification. After preliminary data processing, the objects are classified. The paper lists and describes such classification methods as nuclear classification methods, hierarchical divisive classification methods, hierarchical agglomerative classification methods, near neighbor method, far neighbor method, centroid method, group mean method (mean link method) and other issues related to the classification of geoinformation objects.

scholarly journals Investigation into the Classification of Tight Sandstone Reservoirs via Imbibition Characteristics

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2619 ◽  
Author(s):  
Ming Li ◽  
Hai’en Yang ◽  
Hongjun Lu ◽  
Tianjiang Wu ◽  
Desheng Zhou ◽  
...  
Keyword(s):  
Steady State ◽  
Oil Recovery ◽  
Classification Methods ◽  
Tight Sandstone ◽  
The Third ◽  
Development Regime ◽  
Sandstone Reservoirs ◽  
Second Category ◽  
First Time

Tight sandstone reservoirs are often produced by shutting in the well and inducing imbibition. However, by adopting current reservoir classifications, the heterogeneity of reservoirs cannot be properly treated. Based upon the analysis of the imbibition curves and mercury intrusion porosimetry tests, Chang-7 tight sandstone reservoirs were classified into three categories according to the newly proposed standards. Imbibition tests demonstrated that for the first category, imbibition and drainage occurred continuously and never reached the plateau within the experiment duration. It was suggested that a longer shut-in time favors the production of oil. For the second category, a steady state for imbibition was reached and a shut-in time as short as three days resulted in a high imbibition rate. For the third category, a plateau was reached for the first time and imbibition restarted until a steady state was reached. The average shut-in time for the third category was eight days. Compatibility between reservoir characteristics and a soaking development regime based upon the proposed classification methods effectively enhances the oil recovery efficiency of formations with distinct petrophysical properties. This provides insight into the classification methods of tight sandstone reservoirs.

scholarly journals ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES

2016 ◽  
Vol 9 (5) ◽  
pp. 91 ◽  
Author(s):  
Sunil Kumar P ◽  
Harikumar Rajaguru
Keyword(s):  
Singular Value Decomposition ◽  
Dimensionality Reduction ◽  
Neurological Diseases ◽  
Approximate Entropy ◽  
Singular Value ◽  
Eeg Signals ◽  
Risk Levels ◽  
Value Decomposition ◽  
The Brain

ABSTRACTObjective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it usingvarious post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activitiesof the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is usedby both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis ofmany neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalentneurological disorders is epilepsy and it is easily characterized by recurrent seizures.Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent ComponentAnalysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF).The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and WeightedKNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion: Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was whenLDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risklevels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types ofclassifiers for the perfect classification of epilepsy risk levels from EEG signals.Keywords: FMI, ICA, LGE, LDA, W-KNN, EEG

Feature Extraction and Classification of the Electric Current Signal of an Induction Motor for Condition Monitoring Purposes

2016 ◽  
Vol 856 ◽  
pp. 244-251
Author(s):  
Christian Gebbe ◽  
Christin Tran ◽  
Florian Lingenfelser ◽  
Johannes Glasschröder ◽  
Gunther Reinhart
Keyword(s):  
Feature Extraction ◽  
Induction Motor ◽  
Current Data ◽  
High Availability ◽  
Signal Acquisition ◽  
Classification Methods ◽  
Current Signal ◽  
Data Set ◽  
Motor Current

A high availability of machines has always been important in production. One way to increase it is to avoid unscheduled production stops by detecting the onset of machine faults and to conduct preventative repairs. The detection part consists of the three steps signal acquisition, feature extraction and classification. This paper focuses on the last two steps through the example of an induction motor. Based on a publicly available motor current data set, features were extracted using the continuous wavelet transform. In the subsequent classification step eight different classification methods were compared with each other. It was found, that the accuracy of the classifiers varied significantly in a range from 20.6 % to 92.8 %. Moreover, the supportive vector machine, scoring an accuracy of 92.8 %, was the only classifier with an accuracy above 55.0 %.

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