scholarly journals Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform

2020 ◽  
Vol 9 (4) ◽  
pp. 744
Author(s):  
Abdelouahad Achmamad ◽  
Abdelali Belkhou ◽  
Atman Jbari
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Neuromuscular Disorders ◽  
Computation Time ◽  
Support Vector ◽  
Q Factor ◽  
K Nearest Neighbor ◽  
Emg Signal ◽  
Lateral Sclerosis

Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.

scholarly journals Diagnosis of amyotrophic lateral sclerosis (ALS) disorders based on electromyogram (EMG) signal analysis and feature selection

2020 ◽  
Vol 26 (3) ◽  
pp. 155-160
Author(s):  
Aicha Mokdad ◽  
Sidi Mohammed El Amine Debbal ◽  
Fadia Meziani
Keyword(s):  
Nearest Neighbor ◽  
Biceps Brachii ◽  
Neuromuscular Disorders ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Emg Signal ◽  
Frequency Features

AbstractElectromyogram signal (EMG) provides an important source of information for the diagnosis of neuromuscular disorders. In this study, we proposed two methods of analysis which concern the bispectrum and continuous wavelet transform (CWT) of the EMG signal then a comparison is made to select which one is the most suitable to identify an abnormality in biceps brachii muscle in the main purpose is to assess the pathological severity in bifrequency and time-frequency analysis applying respectively bispectrum and CWT. Then four time and frequency features are extracted and three popular machine learning algorithms are implemented to differentiate neuropathy and healthy conditions of the selected muscle. The performance of these time and frequency features are compared using support vector machine (SVM), linear discriminate analysis (LDA) and K-Nearest Neighbor (KNN) classifier performance. The results obtained showed that the SVM classifier yielded the best performance with an accuracy of 95.8%, precision of 92.59% and specificity of 92%. followed by respectively KNN and LDA classifier that achieved respectively an accuracy of 92% and 91.5%, precision of 92% and 85.4%, and specificity of 92% and 83%.

A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs

2020 ◽  
Author(s):  
Hoda Heidari ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Iir Filters ◽  
Wide Range ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.

scholarly journals Performance Analysis of Supervised Learning Models for Product Title Classification

2019 ◽  
Vol 8 (3) ◽  
pp. 228 ◽  
Author(s):  
Norsyela Muhammad Noor Mathivanan ◽  
Nor Azura Md.Ghani ◽  
Roziah Mohd Janor
Keyword(s):  
Supervised Learning ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Classification Problem ◽  
Short Description ◽  
Support Vector ◽  
Data Sets ◽  
Learning Models ◽  
K Nearest Neighbor ◽  
Online Business

<p>Online business development through e-commerce platforms is a phenomenon which change the world of promoting and selling products in this 21<sup>st</sup> century. Product title classification is an important task in assisting retailers and sellers to list a product in a suitable category. Product title classification is apart of text classification problem but the properties of product title are different from general document. This study aims to evaluate the performance of five different supervised learning models on data sets consist of e-commerce product titles with a very short description and they are incomplete sentences. The supervised learning models involve in the study are Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM) and Random Forest. The results show KNN model is the best model with the highest accuracy and fastest computation time to classify the data used in the study. Hence, KNN model is a good approach in classifying e-commerce products.</p>

A comparison of the analysis of methods for feature extraction and classification by Wavelet transform in SSVEP BCIs

2021 ◽  
Author(s):  
Hoda Heidari ◽  
zahra einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Vector Machines ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods which includes 1) feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) and wavelet transform magnitude, 2) feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), 3) classification step applying k nearest neighbor (k-NN), support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down. The highest level of accuracy was obtained 91.39%.

scholarly journals A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

2020 ◽  
Vol 9 (4) ◽  
pp. 1420-1429
Author(s):  
Abdelouahad Achmamad ◽  
Atman Jbari
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Neuromuscular Disorders ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Emg Signal ◽  
Selection Of

Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.

scholarly journals EMG Signal Based Pattern Recognition Of Grasping Movement Using MODWT And Weighted K- Nearest Neighbor

2019 ◽  
Vol 8 (10) ◽  
pp. 1759-1764
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Signal Processing Technique ◽  
Semg Signal ◽  
Emg Signal ◽  
Semg Signals

This study purposed and evaluates a method based on weighted K-NN classification of surface Electromyogram (sEMG) signals. The sEMG signal classification plays the key role in designing a prosthetic for amputee persons. Wavelet transform is new signal processing technique, which provides better resolution in time and frequency domain simultaneously. Due to these wavelet properties, it can be effectively used in processing the sEMG signal to determine certain amplitude changes at certain frequencies. This paper propose a Maximal overlap Discrete Wavelet Transform (MODWT) approach for Weighted K-NN classifier for classification of sEMG signals based Grasping movements. At level 5 signal decomposition using MODWT, useful resolution component of the sEMG signal is obtained. In this paper Time-domain (TD) features set is used, which shows a decent performance. In WKNN, use a square-inverse weighted technique to improve the performance of the K-NN. Hence, a novel feature set obtained from decomposed signal using MODWT is used to improve the performance of sEMG for classification. MODWT was used for de-noising and time scale feature extraction of sEMG signals. Several WKNN classifiers are tested to optimize classification accuracy and computational problems. PCA is use to reduce the size of the level 5 decomposed data. WKNN performance evaluation on K=10 values with or without PCA. Six hand grasping movements have been classified, results indicate that this method allows the classification of hand pattern recognition with high precision.

scholarly journals Application-Specific Discriminant Analysis of Cardiac Anomalies Using Shift-Invariant Wavelet Transform

2021 ◽  
Vol 12 (4) ◽  
pp. 76-96
Author(s):  
Ritu Singh ◽  
Navin Rajpal ◽  
Rajesh Mehta
Keyword(s):  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Medical Application ◽  
Computational Time ◽  
Support Vector ◽  
Testing Time ◽  
K Nearest Neighbor ◽  
Complex Wavelet Transform ◽  
Linear Discriminant

Automatic arrhythmia detection in electrocardiogram (ECG) using supervised learning has gained significant considerations in recent years. This paper projects the performance analysis of classifiers such as support vector machine (SVM), extreme learning machine (ELM), and k-nearest neighbor (KNN) with efficient time utilization showing multi-classification for specific medical application. The wavelet double decomposition is used to show the shift-invariant use of dual-tree complex wavelet transform for noise filtering and beat segmentation is done to extract 130 informative samples. Further, the linear discriminant analysis is applied to dimensionally reduce and elite the 12 most relevant features for classifying normal and four abnormal beats collected from MIT/BIH ECG database. The proposed executed system distinguishes SVM, ELM, and KNN with percentage accuracy of 99.8, 97, and 99.8 having classifier testing time as 0.0081s, 0.0031s, and 0.0234s, respectively. The simulated experimental outcomes in comparison with existing work yields adequate accuracy, and computational time.

scholarly journals Myopathy Detection and Classification Based on the Continuous Wavelet Transform

2019 ◽  
Vol 15 (4) ◽  
Author(s):  
Abdelali Belkhou ◽  
Abdelouahed Achmamad ◽  
Atman Jbari
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Nearest Neighbor ◽  
Neuromuscular Diseases ◽  
Kernel Functions ◽  
Support Vector ◽  
Continuous Wavelet ◽  
K Nearest Neighbor ◽  
Signal Characteristics

Electromyography (EMG) is the study of the electrical activity of the muscle. This technique is often used in the diagnosis of neuromuscular diseases. Myopathy is one of these cases, which affect the muscle and causes many changes in the electromyography signal characteristics. This paper presents a new method for analysis and classification of normal and myopathy EMG signals based on continuous wavelet transform (CWT). Classification algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Discriminant Analysis (DA) and Naïve Bayes (NB) were used as classifiers in our study. Five Features were extracted from the continuous wavelet analysis and used as inputs to the mentioned classifiers. Comparison between different classification methods developed in this study was made by evaluation of their results based on multiple scalar performances, mainly accuracy, sensitivity, and specificity. Different combinations of features with different kernel functions were discussed to achieve better performances. Results showed that k-NN classifier achieved the best performances with an accuracy value of 93.68%.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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