Automated classification of hand movements using tunable-Q wavelet transform based filter-bank with surface electromyogram signals

2019 ◽  
Vol 93 ◽  
pp. 96-110 ◽  
Author(s):  
Anurag Nishad ◽  
Abhay Upadhyay ◽  
Ram Bilas Pachori ◽  
U. Rajendra Acharya
Keyword(s):  
Wavelet Transform ◽  
Filter Bank ◽  
Hand Movements ◽  
Automated Classification ◽  
Surface Electromyogram

Automated Classification of Glaucoma Stages Using Flexible Analytic Wavelet Transform From Retinal Fundus Images

2020 ◽  
Vol 20 (21) ◽  
pp. 12885-12894 ◽  
Author(s):  
Deepak Parashar ◽  
Dheeraj Kumar Agrawal
Keyword(s):  
Wavelet Transform ◽  
Fundus Images ◽  
Automated Classification ◽  
Analytic Wavelet ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automated classification of stellar spectra based on PCA and wavelet transform

2001 ◽  
Author(s):  
Dongmei Qin ◽  
Zhanyi Hu ◽  
Yongheng Zhao
Keyword(s):  
Wavelet Transform ◽  
Automated Classification ◽  
Stellar Spectra

Classification of hand movements using variational mode decomposition and composite permutation entropy index with surface electromyogram signals

2020 ◽  
Vol 110 ◽  
pp. 1023-1036 ◽  
Author(s):  
Feiyun Xiao ◽  
Decai Yang ◽  
Zhongming Lv ◽  
Xiaohui Guo ◽  
Zhengshi Liu ◽  
...  
Keyword(s):  
Hand Movements ◽  
Permutation Entropy ◽  
Variational Mode Decomposition ◽  
Surface Electromyogram ◽  
Entropy Index ◽  
Mode Decomposition

CLASSIFICATION OF FOCAL AND NONFOCAL EEG SIGNALS USING FEATURES DERIVED FROM FOURIER-BASED RHYTHMS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740002 ◽  
Author(s):  
PUSHPENDRA SINGH ◽  
RAM BILAS PACHORI
Keyword(s):  
Filter Bank ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
Eeg Signals ◽  
Input Feature ◽  
The Mean ◽  
A New Technique ◽  
Electroencephalogram Eeg

We propose a new technique for the automated classification of focal and nonfocal electroencephalogram (EEG) signals using Fourier-based rhythms in this paper. The EEG rhythms, namely, delta, theta, alpha, beta and gamma, are obtained using the discrete Fourier transform (DFT)-based filter bank applied on EEG signals. The mean-frequency (MF) and root-mean-square (RMS) bandwidth features are derived using DFT-based computation on rhythms of EEG signals and their envelopes. These derived features, namely, MF and RMS bandwidths have been provided as an input feature set for the classification of focal and nonfocal EEG signals using a least-squares support vector machine (LS-SVM) classifier. We present experimental results obtained from the publicly available database in order to demonstrate the effectiveness of the proposed feature sets for the automated classification of the focal and nonfocal classes of EEG signals. The obtained classification accuracy in this dataset for the automated classification of focal and nonfocal 50 pairs and 750 pairs of EEG signals are 89.7% and 89.52%, respectively.

FRACTAL PROPERTIES OF SURFACE ELECTROMYOGRAM FOR CLASSIFICATION OF LOW-LEVEL HAND MOVEMENTS FROM SINGLE-CHANNEL FOREARM MUSCLE ACTIVITY

2011 ◽  
Vol 11 (03) ◽  
pp. 581-590 ◽  
Author(s):  
SRIDHAR P. ARJUNAN ◽  
DINESH K. KUMAR
Keyword(s):  
Muscle Contraction ◽  
Muscle Activity ◽  
Single Channel ◽  
Computer Interface ◽  
Hand Movements ◽  
Human Computer Interface ◽  
Wrist Flexion ◽  
Surface Electromyogram ◽  
Fractal Properties

Surface electromyogram (sEMG) has been used in the identification of various hand movements which can lead to a number of rehabilitation, medical, and human computer interface applications. These applications are currently in need of higher accuracy and become challenging because of its unreliability in the classification of sEMG when the level of muscle contraction is low and when there are multiple active muscles. The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when muscles are weakly active such as during sustained wrist and finger flexion. This study reports the use of fractal properties of sEMG to identify the small changes in strength of muscle contraction and the location of the active muscles. It is observed that the fractal dimension (FD) of the signal is related to the complexity of the muscle contraction while maximum fractal length (MFL) is related to the strength of contraction of the associated muscle. The results show that the MFL and FD of a single-channel sEMG from the forearm can be used to accurately identify a set of finger-and-wrist flexion-based actions even when the muscle activity is very weak. It is proposed that such a system could be used to control a prosthetic hand or for a human computer interface.

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