Classification of Alzheimers’ Dementia by Using Various Signal Decomposition Methods

2021 ◽  
Author(s):  
Ozlem Karabiber Cura ◽  
Gulce Cosku Yilmaz ◽  
Hatice Sabiha Ture ◽  
Aydin Akan
Keyword(s):  
Decomposition Methods ◽  
Signal Decomposition

scholarly journals Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

2020 ◽  
Vol 10 (23) ◽  
pp. 8481
Author(s):  
Cesar Federico Caiafa ◽  
Jordi Solé-Casals ◽  
Pere Marti-Puig ◽  
Sun Zhe ◽  
Toshihisa Tanaka
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Unsupervised Classification ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Learning Performance ◽  
Decomposition Approach ◽  
Data Completion ◽  
Machine Learning Applications

In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.

Comparison of EEG signal decomposition methods in classification of motor-imagery BCI

2018 ◽  
Vol 77 (16) ◽  
pp. 21305-21327 ◽  
Author(s):  
Eltaf Abdalsalam Mohamed ◽  
Mohd Zuki Yusoff ◽  
Aamir Saeed Malik ◽  
Mohammad Rida Bahloul ◽  
Dalia Mahmoud Adam ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Eeg Signal

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

EEG Signal Decomposition Evidence for a Role of Perceptual Processes during Conflict-related Behavioral Adjustments in Middle Frontal Regions

2020 ◽  
Vol 32 (7) ◽  
pp. 1381-1393
Author(s):  
Nico Adelhöfer ◽  
Christian Beste
Keyword(s):  
Time Window ◽  
Flanker Task ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Conflict Monitoring ◽  
Stimulus Response ◽  
Eeg Data ◽  
Congruency Sequence Effect ◽  
Trial History

Conflict monitoring processes are central to cope with fluctuating environmental demands. However, the efficacy of these processes depends on previous trial history/experience, which is reflected in the “congruency sequence effect” (CSE). Several theoretical accounts have been put forward to explain this effect. Some accounts stress the role of perceptual processes in the emergence of the CSE. As yet, it is elusive how these perceptual processes are implemented on a neural level. We examined this question using a newly developed moving dots flanker task. We combine decomposition methods of EEG data and source localization. We show that perceptual processes modulate the CSE and can be isolated in neurophysiological signals, especially in the N2 ERP time window. However, mechanisms relating perception to action are also coded and modulated in this time window. We show that middle frontal regions (BA 6) are associated with processes dealing with purely perceptual processes. Inferior frontal regions (BA 45) are associated with processes dealing with stimulus–response transition processes. Likely, the neurophysiological modulations reflect unbinding processes at the perceptual level, and stimulus–response translation level needed to respond correctly on the presented (changed) stimulus–response relationships. The data establish a direct relationship between psychological concepts focusing on perceptual processes during conflict monitoring and neurophysiological processes using signal decomposition.

scholarly journals Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5297
Author(s):  
Jie Lv ◽  
Wenlei Sun ◽  
Hongwei Wang ◽  
Fan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Search Algorithm ◽  
Decomposition Methods ◽  
Sample Entropy ◽  
Signal Decomposition ◽  
Support Vector ◽  
Optimal Parameters ◽  
Rolling Bearings ◽  
Signal Features ◽  
Mode Decomposition

We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

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